ISYE 6501 Final Quiz – Questions & Verified Answers
ISYE 6501: Introduction to Analytics Modeling
SECTION 1: SUPERVISED VS. UNSUPERVISED LEARNING (Questions 1–15)
Question 1
Which of the following is an example of a supervised learning problem?
A) Grouping customers into segments based on purchasing behavior
B) Predicting house prices based on square footage and number of bedrooms
C) Reducing the dimensionality of a dataset while preserving variance
D) Finding association rules in market basket data
Answer: B
Rationale: Supervised learning uses labeled data to predict an outcome.
Predicting house prices (a continuous outcome) from features is supervised
regression. Clustering (A) and association rules (D) are unsupervised learning. PCA
(C) is dimensionality reduction, also unsupervised .
Question 2
,Which of the following is an example of an unsupervised learning task?
A) Predicting whether an email is spam or not
B) Estimating house prices
C) Grouping news articles into topics
D) Forecasting stock prices
Answer: C
Rationale: Topic modeling (grouping articles into topics without pre-existing
labels) is unsupervised learning. Spam detection (A), price prediction (B), and
stock forecasting (D) are all supervised problems because they use labeled
historical data .
Question 3
In supervised learning, what do "labels" refer to?
A) The input features used for prediction
B) The target variable or outcome we want to predict
C) The names of the data columns
,D) The unique identifiers for each observation
Answer: B
Rationale: Labels are the target variables (outcomes) in supervised learning—
what we are trying to predict. Features or predictors (A) are the inputs, not the
labels. Column names (C) and IDs (D) are metadata .
Question 4
Semi-supervised learning uses:
A) Only labeled data
B) Only unlabeled data
C) Some labeled data and some unlabeled data
D) No data at all
Answer: C
, Rationale: Semi-supervised learning combines a small amount of labeled data
with a large amount of unlabeled data during training. This approach is useful
when labeling is expensive but unlabeled data is abundant .
Question 5
Reinforcement learning uses which mechanism to learn optimal behavior?
A) Labeled examples
B) Unsupervised clustering
C) Rewards and punishments
D) Dimensionality reduction
Answer: C
Rationale: Reinforcement learning agents learn through trial and error, receiving
rewards for desirable actions and punishments for undesirable ones. This is
distinct from supervised learning (labeled examples) and unsupervised learning
(clustering, dimensionality reduction) .
Question 6
ISYE 6501: Introduction to Analytics Modeling
SECTION 1: SUPERVISED VS. UNSUPERVISED LEARNING (Questions 1–15)
Question 1
Which of the following is an example of a supervised learning problem?
A) Grouping customers into segments based on purchasing behavior
B) Predicting house prices based on square footage and number of bedrooms
C) Reducing the dimensionality of a dataset while preserving variance
D) Finding association rules in market basket data
Answer: B
Rationale: Supervised learning uses labeled data to predict an outcome.
Predicting house prices (a continuous outcome) from features is supervised
regression. Clustering (A) and association rules (D) are unsupervised learning. PCA
(C) is dimensionality reduction, also unsupervised .
Question 2
,Which of the following is an example of an unsupervised learning task?
A) Predicting whether an email is spam or not
B) Estimating house prices
C) Grouping news articles into topics
D) Forecasting stock prices
Answer: C
Rationale: Topic modeling (grouping articles into topics without pre-existing
labels) is unsupervised learning. Spam detection (A), price prediction (B), and
stock forecasting (D) are all supervised problems because they use labeled
historical data .
Question 3
In supervised learning, what do "labels" refer to?
A) The input features used for prediction
B) The target variable or outcome we want to predict
C) The names of the data columns
,D) The unique identifiers for each observation
Answer: B
Rationale: Labels are the target variables (outcomes) in supervised learning—
what we are trying to predict. Features or predictors (A) are the inputs, not the
labels. Column names (C) and IDs (D) are metadata .
Question 4
Semi-supervised learning uses:
A) Only labeled data
B) Only unlabeled data
C) Some labeled data and some unlabeled data
D) No data at all
Answer: C
, Rationale: Semi-supervised learning combines a small amount of labeled data
with a large amount of unlabeled data during training. This approach is useful
when labeling is expensive but unlabeled data is abundant .
Question 5
Reinforcement learning uses which mechanism to learn optimal behavior?
A) Labeled examples
B) Unsupervised clustering
C) Rewards and punishments
D) Dimensionality reduction
Answer: C
Rationale: Reinforcement learning agents learn through trial and error, receiving
rewards for desirable actions and punishments for undesirable ones. This is
distinct from supervised learning (labeled examples) and unsupervised learning
(clustering, dimensionality reduction) .
Question 6
