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QMB3302 Final UF Questions and Answers
Select all that apply. Imagine you have a data set with
columns/inputs for customers:
Column 1 = Customer ID (a number)
Column 2 = Sales (a dollar value)
Column 3= Frequency (a number)
Column 4 = Satisfaction (a number)
You would like to understand the impact of Frequency on
customer Satisfaction. What types of approaches could you use?
Note that the type of data is brackets () after the column name.
Ans: Decision tree, random forest, linear regression
Select all that apply. Imagine you have a dataset with the
following columns (inputs) for a set of customers.
Column 1 = Customer ID
Column 2 = Distance to Store
Column 3= Yearly spend
Column 4 = Likelihood to return (a survey response that indicates
a customer is likely to shop again)
What kind of approaches could you use to understand more about
these customers? Why?
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Ans: Regression - to udnerstand the effect of one or more variables on
the others
Clustering-to develop groups of customers that have similar patterns
In kmeans- the algorithm has multiple iterations. If we have a
simple 2d problem, and a k =2. After the initial centroid,
_________________ of each point or record to it after
Ans: Measuring the distance
Neural networks are an unsupervised technique, because there is
no target variable. (True or False)
Ans: False
When viewing a diagram of a neural network there are several
layers. The hidden layer:
Ans: Something you dont see, here there is some computation to
transform X into the Y
Recommenders come in many flavors. 2 of the most common,
often used together and discussed in the lecture are:
Ans: User based and Item based
The LinearRegression estimator is only capable of simple straight
line fits. (True or false)
Ans: False
In class we walked through 5 steps to building a machine learning
model. The textbook also goes over in some depth the 5 steps.
What is step 1?
Ans: Choosing a class of model
In class we walked through 5 steps to building a machine learning
model. The textbook also goes over in some depth the 5 steps.
What is step 2?
© 2025 All rights reserved
QMB3302 Final UF Questions and Answers
Select all that apply. Imagine you have a data set with
columns/inputs for customers:
Column 1 = Customer ID (a number)
Column 2 = Sales (a dollar value)
Column 3= Frequency (a number)
Column 4 = Satisfaction (a number)
You would like to understand the impact of Frequency on
customer Satisfaction. What types of approaches could you use?
Note that the type of data is brackets () after the column name.
Ans: Decision tree, random forest, linear regression
Select all that apply. Imagine you have a dataset with the
following columns (inputs) for a set of customers.
Column 1 = Customer ID
Column 2 = Distance to Store
Column 3= Yearly spend
Column 4 = Likelihood to return (a survey response that indicates
a customer is likely to shop again)
What kind of approaches could you use to understand more about
these customers? Why?
© 2025 All rights reserved
, 2 | Page
Ans: Regression - to udnerstand the effect of one or more variables on
the others
Clustering-to develop groups of customers that have similar patterns
In kmeans- the algorithm has multiple iterations. If we have a
simple 2d problem, and a k =2. After the initial centroid,
_________________ of each point or record to it after
Ans: Measuring the distance
Neural networks are an unsupervised technique, because there is
no target variable. (True or False)
Ans: False
When viewing a diagram of a neural network there are several
layers. The hidden layer:
Ans: Something you dont see, here there is some computation to
transform X into the Y
Recommenders come in many flavors. 2 of the most common,
often used together and discussed in the lecture are:
Ans: User based and Item based
The LinearRegression estimator is only capable of simple straight
line fits. (True or false)
Ans: False
In class we walked through 5 steps to building a machine learning
model. The textbook also goes over in some depth the 5 steps.
What is step 1?
Ans: Choosing a class of model
In class we walked through 5 steps to building a machine learning
model. The textbook also goes over in some depth the 5 steps.
What is step 2?
© 2025 All rights reserved
