ISYE6501 INTRODUCTION ANALYTICS
MODELING COMPREHENSIVE TEST BANK
2026 COMPLETE QUESTIONS AND
VERIFIED ANSWERS 100% CORRECT
◉ What do predictive questions ask? Answer: What will happen? (e.g.,
what will Google's stock price be?)
◉ What do prescriptive questions ask? Answer: What action(s) would
be best? (e.g., where to put traffic lights)
◉ What is a model? Answer: Real-life situation expressed as math.
◉ What do classifiers help you do? Answer: differentiate
◉ What is a soft classifier and when is it used? Answer: In some cases,
there won't be a line that separates all of the labeled examples. So we
use a classifier that minimizes the number of mistakes.
◉ What does it mean when the classifier/decision boundary is almost
parallel to the vertical x-axis? Answer: The horizontal attribute is all that
is needed.
◉ What does it mean when the classifier/decision boundary is almost
parallel to the horizontal y-axis? Answer: The vertical attribute is all that
is needed.
,◉ What is time-series data? Answer: The same data recorded over time
often recorded at equal intervals
◉ What is quantitative data? Answer: Number with a meaning: higher
means more, lower means less (e.g., age, sales, temperature, income)
◉ What is categorical data? Answer: Numbers w/o meaning (e.g., zip
codes), non-numeric (e.g., hair color), binary data (e.g., male/female,
yes/no, on/off)
◉ Which of these is time series data?
A. The average cost of a house in the United States every year since
1820
B. The height of each professional basketball player in the NBA at the
start of the season Answer: A
◉ Which of these is structured data?
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account Answer: B
◉ What is structured data? Answer: Data that can be stores in a
structured way
,◉ What is unstructured data? Answer: Data that is not easily described
and stored (e.g., written text)
◉ A survey of 25 people recorded each person's family size and type of
car. Which of these is a data point?
A. The 14th person's family size and car type
B. The 14th person's family size
C.The car type of each person Answer: A.
A data point is all the information about one observation
◉ The farther the wrongly classified point is from the line ___ Answer:
The bigger the mistake we've made
◉ The term including the margin gets larger so the importance of a large
margin out weights avoiding mistakes and classifying known data
samples. Answer: As lambda gets larger
◉ That term also drops towards zero, so the importance of minimizing
mistakes and classifying known data points outweighs having a large
margin. Answer: As lambda drops towards zero
◉ What can SVMs be used for Answer: to find a classifier with
maximum seperation or margin between the two sets of points?
, ◉ When to use SVM? Answer: If it's impossible to avoid classification
errors, SVM can find a classifier that trades off reducing errors and
enlarging the margin.
◉ Error for data point j Answer: What does this formula describe?
◉ Total error Answer: What does this formula describe ?
◉ To maximize the distance between the two lines what do we need to
minimize? Answer:
◉ m_j > 1 Answer: What value do we give for more costly errors
◉ Giving a bad loan is twice as costly as withholding a good loan?
Answer: What does this mean in the context of giving a loan?
◉ m_j < 1 Answer: What value do we give for less costly errors?
◉ Why is it important to scale our data when using SVM? Answer:
We're looking to minimize the sum of the squares of the coefficients, but
if our data has very different scales a small change in one could swamp a
huge change in the other.
◉ what does it signify when a coefficient for a classifier is close to zero
Answer: it means the corresponding attribute is probably not relevant
MODELING COMPREHENSIVE TEST BANK
2026 COMPLETE QUESTIONS AND
VERIFIED ANSWERS 100% CORRECT
◉ What do predictive questions ask? Answer: What will happen? (e.g.,
what will Google's stock price be?)
◉ What do prescriptive questions ask? Answer: What action(s) would
be best? (e.g., where to put traffic lights)
◉ What is a model? Answer: Real-life situation expressed as math.
◉ What do classifiers help you do? Answer: differentiate
◉ What is a soft classifier and when is it used? Answer: In some cases,
there won't be a line that separates all of the labeled examples. So we
use a classifier that minimizes the number of mistakes.
◉ What does it mean when the classifier/decision boundary is almost
parallel to the vertical x-axis? Answer: The horizontal attribute is all that
is needed.
◉ What does it mean when the classifier/decision boundary is almost
parallel to the horizontal y-axis? Answer: The vertical attribute is all that
is needed.
,◉ What is time-series data? Answer: The same data recorded over time
often recorded at equal intervals
◉ What is quantitative data? Answer: Number with a meaning: higher
means more, lower means less (e.g., age, sales, temperature, income)
◉ What is categorical data? Answer: Numbers w/o meaning (e.g., zip
codes), non-numeric (e.g., hair color), binary data (e.g., male/female,
yes/no, on/off)
◉ Which of these is time series data?
A. The average cost of a house in the United States every year since
1820
B. The height of each professional basketball player in the NBA at the
start of the season Answer: A
◉ Which of these is structured data?
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account Answer: B
◉ What is structured data? Answer: Data that can be stores in a
structured way
,◉ What is unstructured data? Answer: Data that is not easily described
and stored (e.g., written text)
◉ A survey of 25 people recorded each person's family size and type of
car. Which of these is a data point?
A. The 14th person's family size and car type
B. The 14th person's family size
C.The car type of each person Answer: A.
A data point is all the information about one observation
◉ The farther the wrongly classified point is from the line ___ Answer:
The bigger the mistake we've made
◉ The term including the margin gets larger so the importance of a large
margin out weights avoiding mistakes and classifying known data
samples. Answer: As lambda gets larger
◉ That term also drops towards zero, so the importance of minimizing
mistakes and classifying known data points outweighs having a large
margin. Answer: As lambda drops towards zero
◉ What can SVMs be used for Answer: to find a classifier with
maximum seperation or margin between the two sets of points?
, ◉ When to use SVM? Answer: If it's impossible to avoid classification
errors, SVM can find a classifier that trades off reducing errors and
enlarging the margin.
◉ Error for data point j Answer: What does this formula describe?
◉ Total error Answer: What does this formula describe ?
◉ To maximize the distance between the two lines what do we need to
minimize? Answer:
◉ m_j > 1 Answer: What value do we give for more costly errors
◉ Giving a bad loan is twice as costly as withholding a good loan?
Answer: What does this mean in the context of giving a loan?
◉ m_j < 1 Answer: What value do we give for less costly errors?
◉ Why is it important to scale our data when using SVM? Answer:
We're looking to minimize the sum of the squares of the coefficients, but
if our data has very different scales a small change in one could swamp a
huge change in the other.
◉ what does it signify when a coefficient for a classifier is close to zero
Answer: it means the corresponding attribute is probably not relevant
