ISYE 6501 MIDTERM 1 EXAM| Questions
and Answers | 2026 Update | 100% Correct.-GT
SECTION 1: SUPERVISED vs. UNSUPERVISED
LEARNING (Questions 1-6)
Question 1
In the context of statistical learning, what is the primary goal of supervised
learning?
A. To find hidden structures in unlabeled data
B. To maximize the reward in a dynamic environment
C. To model the relationship between a response variable and a set of
predictors
D. To reduce the dimensionality of the data while preserving variance
Correct Answer: C. To model the relationship between a response variable
and a set of predictors
Rationale: Supervised learning involves building a model that relates a
response (dependent variable) to one or more predictors (independent
variables) using labeled training data . The key is that we "know the right
classification for the data points" already knowing the response .
Subtopic: Supervised vs. Unsupervised Learning
Question 2
A group of astronomers has a set of long-exposure CCD images of various
distant objects. They do not know yet which types of objects each one is.
Which is more appropriate?
,A. Classification (supervised)
B. Clustering (unsupervised)
C. Linear regression
D. ARIMA
Correct Answer: B. Clustering (unsupervised)
Rationale: In clustering (unsupervised learning), we know the attributes but
don't know what group any of the data points are in. The model must decide
how to cluster based only on attributes of the data . Since the astronomers
do not have pre-labeled categories, clustering is appropriate.
Subtopic: Unsupervised Learning Applications
Question 3
Which of the following is a typical example of a classification problem?
A. Predicting the price of a stock
B. Grouping customers into market segments
C. Diagnosing whether a patient has a disease
D. Estimating the number of units sold
Correct Answer: C. Diagnosing whether a patient has a disease
Rationale: Classification problems involve predicting a categorical outcome,
such as the presence or absence of a disease . Stock price prediction and
estimating units sold are regression problems (continuous outcomes).
Grouping customers is clustering (unsupervised).
Subtopic: Classification Problems
Question 4
What do descriptive questions ask?
A. What will happen?
B. What happened? (e.g., which customers are most alike)
C. How can we make it happen?
D. Why did it happen?
, Correct Answer: B. What happened? (e.g., which customers are most alike)
Rationale: Descriptive analytics answers "What happened?" Predictive
answers "What will happen?" Prescriptive answers "How can we make it
happen?" Understanding these categories is fundamental for choosing
appropriate models.
Subtopic: Analytics Categories
Question 5
What do predictive questions ask?
A. What happened?
B. What will happen? (e.g., what will Google's stock price be?)
C. How can we make it happen?
D. Why did it happen?
Correct Answer: B. What will happen? (e.g., what will Google's stock price
be?)
Rationale: Predictive analytics focuses on forecasting future outcomes based
on historical data patterns. This includes models like time series forecasting
and regression .
Subtopic: Analytics Categories
Question 6
If we use the same data to fit a model as we do to estimate how good it is,
what is likely to happen?
A. The model will appear to be worse than it really is
B. The model will appear to be about the same as it really is
C. The model will appear to be better than it really is
D. The model will overfit but performance estimate will be accurate
Correct Answer: C. The model will appear to be better than it really is
Rationale: The model will be fit to both real and random patterns in the data.
Its effectiveness on the training data set will include both types of patterns,
and Answers | 2026 Update | 100% Correct.-GT
SECTION 1: SUPERVISED vs. UNSUPERVISED
LEARNING (Questions 1-6)
Question 1
In the context of statistical learning, what is the primary goal of supervised
learning?
A. To find hidden structures in unlabeled data
B. To maximize the reward in a dynamic environment
C. To model the relationship between a response variable and a set of
predictors
D. To reduce the dimensionality of the data while preserving variance
Correct Answer: C. To model the relationship between a response variable
and a set of predictors
Rationale: Supervised learning involves building a model that relates a
response (dependent variable) to one or more predictors (independent
variables) using labeled training data . The key is that we "know the right
classification for the data points" already knowing the response .
Subtopic: Supervised vs. Unsupervised Learning
Question 2
A group of astronomers has a set of long-exposure CCD images of various
distant objects. They do not know yet which types of objects each one is.
Which is more appropriate?
,A. Classification (supervised)
B. Clustering (unsupervised)
C. Linear regression
D. ARIMA
Correct Answer: B. Clustering (unsupervised)
Rationale: In clustering (unsupervised learning), we know the attributes but
don't know what group any of the data points are in. The model must decide
how to cluster based only on attributes of the data . Since the astronomers
do not have pre-labeled categories, clustering is appropriate.
Subtopic: Unsupervised Learning Applications
Question 3
Which of the following is a typical example of a classification problem?
A. Predicting the price of a stock
B. Grouping customers into market segments
C. Diagnosing whether a patient has a disease
D. Estimating the number of units sold
Correct Answer: C. Diagnosing whether a patient has a disease
Rationale: Classification problems involve predicting a categorical outcome,
such as the presence or absence of a disease . Stock price prediction and
estimating units sold are regression problems (continuous outcomes).
Grouping customers is clustering (unsupervised).
Subtopic: Classification Problems
Question 4
What do descriptive questions ask?
A. What will happen?
B. What happened? (e.g., which customers are most alike)
C. How can we make it happen?
D. Why did it happen?
, Correct Answer: B. What happened? (e.g., which customers are most alike)
Rationale: Descriptive analytics answers "What happened?" Predictive
answers "What will happen?" Prescriptive answers "How can we make it
happen?" Understanding these categories is fundamental for choosing
appropriate models.
Subtopic: Analytics Categories
Question 5
What do predictive questions ask?
A. What happened?
B. What will happen? (e.g., what will Google's stock price be?)
C. How can we make it happen?
D. Why did it happen?
Correct Answer: B. What will happen? (e.g., what will Google's stock price
be?)
Rationale: Predictive analytics focuses on forecasting future outcomes based
on historical data patterns. This includes models like time series forecasting
and regression .
Subtopic: Analytics Categories
Question 6
If we use the same data to fit a model as we do to estimate how good it is,
what is likely to happen?
A. The model will appear to be worse than it really is
B. The model will appear to be about the same as it really is
C. The model will appear to be better than it really is
D. The model will overfit but performance estimate will be accurate
Correct Answer: C. The model will appear to be better than it really is
Rationale: The model will be fit to both real and random patterns in the data.
Its effectiveness on the training data set will include both types of patterns,
