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Business Intelligence, Analytics, Data Science, and AI, 5th Edition by Ramesh Sharda, Dursun Delen, Efraim Turban Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms€9,05
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Business Intelligence, Analytics, Data Science, An
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Business Intelligence, Analytics, Data Science, and AI, 5th Edition by Ramesh Sharda, Dursun Delen, Efraim Turban Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms
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Business Intelligence, Analytics, Data Science, An
Business Intelligence, Analytics, Data Science, and AI, 5th Edition by Ramesh Sharda, Dursun Delen, Efraim Turban
Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms
1) Data mining is a way to develop intelligence (i.e., actionable information or knowledge) from tex...
Business Intelligence, Analytics, Data Science, an
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Business Intelligence, Analytics, Data Science,
and AI, 5th Edition by Ramesh Sharda, Dursun
Delen, Efraim Turban
Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms
1) Data mining is a way to develop intelligence (i.e., actionable information or knowledge) from
textual data that an organization collects, organizes, and stores.
Answer: FALSE
Diff: 2 Page Ref: 279
2) The cost of data storage has plummeted recently, initiating the amount of data stored in
electronic form grown at an explosive rate, making data mining feasible for more firms.
Answer: TRUE
Diff: 2 Page Ref: 284
3) Data mining can be very useful in detecting patterns such as credit card fraud but is of little
help in improving sales.
Answer: FALSE
Diff: 1 Page Ref: 284
4) If using a mining analogy, "knowledge mining" would be a more appropriate term than "data
mining."
Answer: TRUE
Diff: 2 Page Ref: 286
5) Consolidation of databases and other data repositories into a single location in the form of a
data warehouse had no impact on increased popularity of data mining.
Answer: FALSE
Diff: 1 Page Ref: 284
6) Data mining requires specialized data analysts to ask ad hoc questions and obtain descriptive
answers quickly from the varied data system.
Answer: FALSE
Diff: 2 Page Ref: 287
7) Confusion matrix can only be used for binary classification problems where there are two
outcomes, such as true and false for the target variable.
Answer: FALSE
Diff: 1 Page Ref: 303
9) Bayesian classifiers use probability theory to build classification models based on past
occurrences that are capable of placing a new instance into a most probable class.
Answer: TRUE
Diff: 2 Page Ref: 306
10) In data mining, classification models help in prediction of cases that belong to two or more
categories.
Answer: TRUE
Diff: 2 Page Ref: 288
11) Statistics and data mining both look for data sets that are as large and as varied as possible.
Answer: FALSE
Diff: 2 Page Ref: 291
12) In CRISP-DM, testing and evaluation step is not focused on evaluating the accuracy of
different models, rather it is about assessing whether the business problem is appropriately
addressed.
Answer: TRUE
Diff: 1 Page Ref: 299
13) In KDD standardized process, data mining encompasses the whole process, not just one step.
Answer: FALSE
Diff: 2 Page Ref: 301
14) During classification in data mining, a false positive is an occurrence classified as positive by
the algorithm while being negative in reality.
Answer: TRUE
Diff: 1 Page Ref: 302
15) k-fold cross-validation methodology can also be called rotation estimation.
Answer: TRUE
Diff: 2 Page Ref: 305
16) Bootstrapping is like the k-fold cross-validation where the k takes the value of 1.
Answer: FALSE
Diff: 3 Page Ref: 305
17) The area under the ROC curve is a graphical assessment technique where the true positive
rate is plotted on the y-axis and the false negative rate is plotted on the x-axis.
Answer: FALSE
Diff: 3 Page Ref: 305
18) For classification type predictive modeling, confusion matrix is the primary source for
accuracy estimation.
Answer: TRUE
Diff: 2 Page Ref: 303
, 19) In predictive model assessment, the model's ability to make reasonably accurate predictions,
given noisy data or data with missing and erroneous values is called scalability.
Answer: FALSE
Diff: 2 Page Ref: 303
20) Data that is collected, stored, and analyzed in data mining is often private and personal. This
type of data needs to go through a process of de-identification prior to applying data mining.
Answer: TRUE
Diff: 2 Page Ref: 322
21) Data mining provides instant, crystal-ball-like insights is nothing but a myth.
Answer: TRUE
Diff: 2 Page Ref: 324
22) K-means is the most popular algorithm used for association rule mining.
Answer: FALSE
Diff: 2 Page Ref: 312-313
23) In data mining, clustering is an exploratory unsupervised data analysis tool for solving
classification problems.
Answer: TRUE
Diff: 2 Page Ref: 310
24) The main idea in market basket analysis is to predict future customer demand patterns so that
an appropriate volume of merchandise would be produced or purchased.
Answer: FALSE
Diff: 2 Page Ref: 313
25) K in K-means clustering refers to the dimensionality of the input data (i.e., the number of
input variables) used in the analysis.
Answer: FALSE
Diff: 2 Page Ref: 312
26) Which of the following is not among the key reasons data mining is gaining attention in the
business world?
A) More intense competition at the global scale
B) Significant increase in mergers and acquisition in the marketplace
C) Recognition of the untapped value hidden in large data sources
D) The exponential increase in data processing and storage technologies
Answer: B
Diff: 2 Page Ref: 284
27) Amazon and others have used analytics to do all of the following, except?
A) Collect data about customers and competitors
B) Better understand their customers
C) Maximize their returns on investment
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