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ISYE 6501 MIDTERM 1 EXAM 2025/2026 | ACTUAL
REAL EXAM QUESTIONS AND ANSWERS | VERIFIED
SOLUTIONS WITH DETAILED EXPLANATIONS |
LATEST UPDATED VERSION
What do predictive questions ask?
What will happen? (e.g., what will Google's stock price be?)
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.
What is time-series data?
The same data recorded over time often recorded at equal intervals
What is quantitative data?
Number with a meaning: higher means more, lower means less (e.g., age, sales, temperature,
income)
What do descriptive questions ask?
What happened? (e.g., which customers are most alike)
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?
Put things into categories
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.
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What is categorical data?
Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair color), binary data (e.g.,
male/female, yes/no, on/off)
A
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
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
That term also drops towards zero, so the importance of minimizing mistakes and classifying
known data points outweighs having a large margin.
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As lambda drops towards zero
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.
Error for data point j
What does this formula describe?
Total error
What does this formula describe ?
m_j > 1
What value do we give for more costly errors
What does this mean in the context of giving a loan?
Giving a bad loan is twice as costly as withholding a good loan?
m_j < 1
What value do we give for less costly errors?
Why is it important to scale our data when using SVM?
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
it means the corresponding attribute is probably not relevant
What do kernel methods allow for in SVMs
nonlinear classifiers
What is the common range for scaled data?
between 0 and 1
What is the formula for min-max scaling?
ISYE 6501 MIDTERM 1 EXAM 2025/2026 | ACTUAL
REAL EXAM QUESTIONS AND ANSWERS | VERIFIED
SOLUTIONS WITH DETAILED EXPLANATIONS |
LATEST UPDATED VERSION
What do predictive questions ask?
What will happen? (e.g., what will Google's stock price be?)
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.
What is time-series data?
The same data recorded over time often recorded at equal intervals
What is quantitative data?
Number with a meaning: higher means more, lower means less (e.g., age, sales, temperature,
income)
What do descriptive questions ask?
What happened? (e.g., which customers are most alike)
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?
Put things into categories
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.
,2|Page
What is categorical data?
Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair color), binary data (e.g.,
male/female, yes/no, on/off)
A
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
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
That term also drops towards zero, so the importance of minimizing mistakes and classifying
known data points outweighs having a large margin.
, 3|Page
As lambda drops towards zero
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.
Error for data point j
What does this formula describe?
Total error
What does this formula describe ?
m_j > 1
What value do we give for more costly errors
What does this mean in the context of giving a loan?
Giving a bad loan is twice as costly as withholding a good loan?
m_j < 1
What value do we give for less costly errors?
Why is it important to scale our data when using SVM?
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
it means the corresponding attribute is probably not relevant
What do kernel methods allow for in SVMs
nonlinear classifiers
What is the common range for scaled data?
between 0 and 1
What is the formula for min-max scaling?
