1|Page
QMB3302 UF FALL Final Exam Version 1,2&3 (3 Latest
Versions) Newest 2025/2026 With Complete Questions
And Correct Answers |Already Graded A+||Latest
Version!!!
The correct number of clusters in Hierarchical clustering
can be determined precisely using approaches such as
silhouette scores (True or False) - Answer-False
In K Means clustering, the analyst does not need to
determine the number of clusters (K), these are always
derived analytically using the kmeans algorithm. (True or
False) - Answer-False
One big difference between the unsupervised approaches
in this module, and the supervised approaches in prior
modules: Unsupervised models do not have a target
variable (Y). This make is difficult to know when they are
"right" or correct. (True or False) - Answer-True
According to the documentation, a silhouette scores of 1 ia
- Answer-The best score
,2|Page
According to the documentation, a silhouette score of -1 is
- Answer-The worst score
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. - Answer-Decision tree, random forest, linear
regression
, 3|Page
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? - Answer-Regression
- to udnerstand the effect of one or more variables on the
others
Clustering-to develop groups of customers that have
similar patterns
What is the purpose of the following code?
from sklearn.preprocessing import StandardScaler
QMB3302 UF FALL Final Exam Version 1,2&3 (3 Latest
Versions) Newest 2025/2026 With Complete Questions
And Correct Answers |Already Graded A+||Latest
Version!!!
The correct number of clusters in Hierarchical clustering
can be determined precisely using approaches such as
silhouette scores (True or False) - Answer-False
In K Means clustering, the analyst does not need to
determine the number of clusters (K), these are always
derived analytically using the kmeans algorithm. (True or
False) - Answer-False
One big difference between the unsupervised approaches
in this module, and the supervised approaches in prior
modules: Unsupervised models do not have a target
variable (Y). This make is difficult to know when they are
"right" or correct. (True or False) - Answer-True
According to the documentation, a silhouette scores of 1 ia
- Answer-The best score
,2|Page
According to the documentation, a silhouette score of -1 is
- Answer-The worst score
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. - Answer-Decision tree, random forest, linear
regression
, 3|Page
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? - Answer-Regression
- to udnerstand the effect of one or more variables on the
others
Clustering-to develop groups of customers that have
similar patterns
What is the purpose of the following code?
from sklearn.preprocessing import StandardScaler
