1|Page
ISYE 6501 Final Exam 2026-2027 BANK QUESTIONS WITH
DETAILED VERIFIED ANSWERS EXAM QUESTIONS WILL
COME FROM HERE (100% CORRECT ANSWERS A+ GRADED
1. What is the primary goal of supervised learning?
A) To find natural groupings in data
B) To predict an output based on input data where the correct output is
known
C) To reduce the dimensionality of the data
D) To find associations between items in a transaction database
Answer: B
Explanation: In supervised learning, the model is trained on a labeled
dataset, meaning each training example is paired with an output label.
The goal is to learn a function that maps inputs to outputs to make
predictions on new, unseen data.
2. Which of the following is an example of an unsupervised learning
task?
A) Predicting housing prices based on square footage
B) Classifying emails as spam or not spam
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C) Segmenting customers into distinct groups based on purchasing
behavior
D) Predicting whether a loan will default
Answer: C
Explanation: Customer segmentation involves grouping customers
based on similarities without pre-existing labels. This is a classic
unsupervised learning task, specifically clustering.
3. In the context of Support Vector Machines (SVM), what is a support
vector?
A) Any data point that is classified correctly
B) The vector connecting the origin to a data point
C) A data point that lies on the margin boundary or is misclassified
D) The weight vector defining the hyperplane
Answer: C
Explanation: Support vectors are the critical data points that define the
position and orientation of the hyperplane. They lie on the edge of the
margin, and removing them would change the SVM solution.
4. What is the purpose of a kernel function in SVM?
A) To decrease the computational complexity of the algorithm
B) To increase the interpretability of the model
C) To implicitly map data into a higher-dimensional space
D) To perform feature selection automatically
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Answer: C
Explanation: A kernel function allows SVM to find a separating
hyperplane in a high-dimensional feature space without explicitly
computing the coordinates of the data in that space, which can be
computationally prohibitive. This is known as the kernel trick.
5. Which model is a non-parametric method?
A) Linear Regression
B) Logistic Regression
C) K-Nearest Neighbors (KNN)
D) Ridge Regression
Answer: C
Explanation: KNN does not assume a fixed parametric form for the
relationship between inputs and outputs. It uses the entire training
dataset at prediction time, making its complexity grow with the data. In
contrast, linear and logistic regression estimate a fixed number of
parameters.
6. In K-Means clustering, how is the center of a cluster typically
defined?
A) The data point closest to all other points in the cluster
B) The mean of all data points assigned to the cluster
C) The median of all data points assigned to the cluster
D) A pre-defined core point for each cluster
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Answer: B
Explanation: The K-Means algorithm iteratively recalculates each
cluster's centroid as the arithmetic mean of all the data points currently
assigned to that cluster, and then reassigns points based on proximity
to the new centroids.
7. What type of data is the following review: "The product is excellent
and works perfectly"?
A) Structured data
B) Unstructured data
C) Time series data
D) Cross-sectional data
Answer: B
Explanation: Text data, like a product review, does not have a pre-
defined data model or organized format (like rows and columns). It is
considered unstructured or semi-structured data.
8. Which metric is most appropriate for evaluating a classification
model when the classes are highly imbalanced?
A) Accuracy
B) Mean Absolute Error
C) AUC (Area Under the ROC Curve)
D) R-squared
Answer: C
ISYE 6501 Final Exam 2026-2027 BANK QUESTIONS WITH
DETAILED VERIFIED ANSWERS EXAM QUESTIONS WILL
COME FROM HERE (100% CORRECT ANSWERS A+ GRADED
1. What is the primary goal of supervised learning?
A) To find natural groupings in data
B) To predict an output based on input data where the correct output is
known
C) To reduce the dimensionality of the data
D) To find associations between items in a transaction database
Answer: B
Explanation: In supervised learning, the model is trained on a labeled
dataset, meaning each training example is paired with an output label.
The goal is to learn a function that maps inputs to outputs to make
predictions on new, unseen data.
2. Which of the following is an example of an unsupervised learning
task?
A) Predicting housing prices based on square footage
B) Classifying emails as spam or not spam
,2|Page
C) Segmenting customers into distinct groups based on purchasing
behavior
D) Predicting whether a loan will default
Answer: C
Explanation: Customer segmentation involves grouping customers
based on similarities without pre-existing labels. This is a classic
unsupervised learning task, specifically clustering.
3. In the context of Support Vector Machines (SVM), what is a support
vector?
A) Any data point that is classified correctly
B) The vector connecting the origin to a data point
C) A data point that lies on the margin boundary or is misclassified
D) The weight vector defining the hyperplane
Answer: C
Explanation: Support vectors are the critical data points that define the
position and orientation of the hyperplane. They lie on the edge of the
margin, and removing them would change the SVM solution.
4. What is the purpose of a kernel function in SVM?
A) To decrease the computational complexity of the algorithm
B) To increase the interpretability of the model
C) To implicitly map data into a higher-dimensional space
D) To perform feature selection automatically
,3|Page
Answer: C
Explanation: A kernel function allows SVM to find a separating
hyperplane in a high-dimensional feature space without explicitly
computing the coordinates of the data in that space, which can be
computationally prohibitive. This is known as the kernel trick.
5. Which model is a non-parametric method?
A) Linear Regression
B) Logistic Regression
C) K-Nearest Neighbors (KNN)
D) Ridge Regression
Answer: C
Explanation: KNN does not assume a fixed parametric form for the
relationship between inputs and outputs. It uses the entire training
dataset at prediction time, making its complexity grow with the data. In
contrast, linear and logistic regression estimate a fixed number of
parameters.
6. In K-Means clustering, how is the center of a cluster typically
defined?
A) The data point closest to all other points in the cluster
B) The mean of all data points assigned to the cluster
C) The median of all data points assigned to the cluster
D) A pre-defined core point for each cluster
, 4|Page
Answer: B
Explanation: The K-Means algorithm iteratively recalculates each
cluster's centroid as the arithmetic mean of all the data points currently
assigned to that cluster, and then reassigns points based on proximity
to the new centroids.
7. What type of data is the following review: "The product is excellent
and works perfectly"?
A) Structured data
B) Unstructured data
C) Time series data
D) Cross-sectional data
Answer: B
Explanation: Text data, like a product review, does not have a pre-
defined data model or organized format (like rows and columns). It is
considered unstructured or semi-structured data.
8. Which metric is most appropriate for evaluating a classification
model when the classes are highly imbalanced?
A) Accuracy
B) Mean Absolute Error
C) AUC (Area Under the ROC Curve)
D) R-squared
Answer: C
