ISYE 6501 ACTUAL FINAL EXAM
2026/2027 WITH QUESTIONS AND
EXPERT-VERIFIED CORRECT
ANSWERS | ALREADY GRADED A+ |
GUARANTEED SUCCESS | ISYE 6501
PRACTICE EXAM
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
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
B
,What is structured data?
Data that can be stores in a structured way
What is unstructured data?
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
A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___
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.
As lambda gets larger
Image: The term including the margin gets larger so the importance of a large
margin out weights avoiding mistakes and classifying known data samples.
,That term also drops towards zero, so the importance of minimizing mistakes and
classifying known data points outweighs having a large margin.
As lambda drops towards zero
Image: That term also drops towards zero, so the importance of minimizing
mistakes and classifying known data points outweighs having a large margin.
What can SVMs be used for
to find a classifier with maximum seperation or margin between the two sets of
points
When to use SVM?
If it's impossible to avoid classification errors, SVM can find a classifier that trades
off reducing errors and enlarging the margin.
What do descriptive questions ask?
What happened? (e.g., which customers are most alike)
What do predictive questions ask?
What will happen? (e.g., what will Google's stock price be?)
, What do prescriptive questions ask?
What action(s) would be best? (e.g., where to put traffic lights)
What is a model?
Real-life situation expressed as math.
What do classifiers help you do?
differentiate
What is a soft classifier and when is it used?
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?
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?
The vertical attribute is all that is needed.
2026/2027 WITH QUESTIONS AND
EXPERT-VERIFIED CORRECT
ANSWERS | ALREADY GRADED A+ |
GUARANTEED SUCCESS | ISYE 6501
PRACTICE EXAM
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
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
B
,What is structured data?
Data that can be stores in a structured way
What is unstructured data?
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
A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___
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.
As lambda gets larger
Image: The term including the margin gets larger so the importance of a large
margin out weights avoiding mistakes and classifying known data samples.
,That term also drops towards zero, so the importance of minimizing mistakes and
classifying known data points outweighs having a large margin.
As lambda drops towards zero
Image: That term also drops towards zero, so the importance of minimizing
mistakes and classifying known data points outweighs having a large margin.
What can SVMs be used for
to find a classifier with maximum seperation or margin between the two sets of
points
When to use SVM?
If it's impossible to avoid classification errors, SVM can find a classifier that trades
off reducing errors and enlarging the margin.
What do descriptive questions ask?
What happened? (e.g., which customers are most alike)
What do predictive questions ask?
What will happen? (e.g., what will Google's stock price be?)
, What do prescriptive questions ask?
What action(s) would be best? (e.g., where to put traffic lights)
What is a model?
Real-life situation expressed as math.
What do classifiers help you do?
differentiate
What is a soft classifier and when is it used?
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?
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?
The vertical attribute is all that is needed.
