1|Page
QMB3302 UF FALL FINAL EXAM VERSION 1,2&3 LATEST 2025/2026
ACTUAL EXAM WITH COMPLETE QUESTIONS AND CORRECT
DETAILED ANSWERS (100% VERIFIED ANSWERS) |ALREADY
GRADED A+| ||PROFESSOR VERIFIED|| ||BRANDNEW!!!||
Which of the following best describes the difference between a
supervised and an unsupervised learning task in machine
learning?
a. A supervised learning task is faster and more efficient than an
unsupervised learning task.
b. A supervised learning task can handle both numerical and
categorical data, while an unsupervised learning task can only
handle numerical data.
c. A supervised learning task requires labeled data, while an
unsupervised task does not.
d. A supervised learning task involves clustering data into groups,
while an unsupervised learning task involves predicting a target
variable. - ANSWER-c
Which is true about linear regression models?
a. They are easy to interpret.
b. They are always the best model to choose.
,2|Page
c. They are the optimal choice of model in a situation where we
have unlabeled data.
d. We want them to completely explain our dataset. - ANSWER-a
Pipelines are useful (in analytics with Python sense) for the
following reasons? (Choose all that apply)
a. Pipelines make it very easy to change small things in your
model, like which variables to include.
b. Pipelines help organize the code you used to clean and treat
your data.
c. Pipelines make it easy to repeat/replicate steps and run
multiple models.
d. Pipelines automatically update to new versions of Python.
e. Pipelines are good for moving data into your programing
environment. - ANSWER-a, b, c
The basic idea of a regression is very simple. We have some X
values (we called these ________) and some Y values (this is the
variable we are trying to _____. We could have multiple Y values,
,3|Page
but that is not something we have covered. - ANSWER-features,
predict
Y and y-hat are a little different. Y is our target vector, and y-hat is
an output in our model that is a(n)......
a. a combination of XY intercept coordinates.
b. estimate or predictions of y.
c. the actual value of y.
d. an axis on our 2 way graph. - ANSWER-b
When looking at the code in the videos, we sometimes used a
variable to hold our model.
What is the significance of the word "model" in the below code?
model = LinearRegression(fit_intercept=True)
a. The word 'model' instantiates the method and calls the
interpreter. Without this specific word, no model functions are
available.
, 4|Page
b. Model is a named variable and is just holding our linear
regression model. It could be renamed anything. The word itself is
not important. It is just a container.
c. The word 'model' calls the fit method. If another word is used in
this example, Python will not understand that it is a model that
can be run. - ANSWER-B
What is a good model fit value?
a. R-squared of .8
b. 99% accurate.
c. 95% accurate.
d. R-squared of p-value minus .05
e. R-squared of .4
f. R-squared of .95
g. Unknowable without knowing/understanding the context and
the domain. - ANSWER-g
Imagine X in the below is a missing value. If I were to run a
median imputer on this set of data what would the returned value
be?
QMB3302 UF FALL FINAL EXAM VERSION 1,2&3 LATEST 2025/2026
ACTUAL EXAM WITH COMPLETE QUESTIONS AND CORRECT
DETAILED ANSWERS (100% VERIFIED ANSWERS) |ALREADY
GRADED A+| ||PROFESSOR VERIFIED|| ||BRANDNEW!!!||
Which of the following best describes the difference between a
supervised and an unsupervised learning task in machine
learning?
a. A supervised learning task is faster and more efficient than an
unsupervised learning task.
b. A supervised learning task can handle both numerical and
categorical data, while an unsupervised learning task can only
handle numerical data.
c. A supervised learning task requires labeled data, while an
unsupervised task does not.
d. A supervised learning task involves clustering data into groups,
while an unsupervised learning task involves predicting a target
variable. - ANSWER-c
Which is true about linear regression models?
a. They are easy to interpret.
b. They are always the best model to choose.
,2|Page
c. They are the optimal choice of model in a situation where we
have unlabeled data.
d. We want them to completely explain our dataset. - ANSWER-a
Pipelines are useful (in analytics with Python sense) for the
following reasons? (Choose all that apply)
a. Pipelines make it very easy to change small things in your
model, like which variables to include.
b. Pipelines help organize the code you used to clean and treat
your data.
c. Pipelines make it easy to repeat/replicate steps and run
multiple models.
d. Pipelines automatically update to new versions of Python.
e. Pipelines are good for moving data into your programing
environment. - ANSWER-a, b, c
The basic idea of a regression is very simple. We have some X
values (we called these ________) and some Y values (this is the
variable we are trying to _____. We could have multiple Y values,
,3|Page
but that is not something we have covered. - ANSWER-features,
predict
Y and y-hat are a little different. Y is our target vector, and y-hat is
an output in our model that is a(n)......
a. a combination of XY intercept coordinates.
b. estimate or predictions of y.
c. the actual value of y.
d. an axis on our 2 way graph. - ANSWER-b
When looking at the code in the videos, we sometimes used a
variable to hold our model.
What is the significance of the word "model" in the below code?
model = LinearRegression(fit_intercept=True)
a. The word 'model' instantiates the method and calls the
interpreter. Without this specific word, no model functions are
available.
, 4|Page
b. Model is a named variable and is just holding our linear
regression model. It could be renamed anything. The word itself is
not important. It is just a container.
c. The word 'model' calls the fit method. If another word is used in
this example, Python will not understand that it is a model that
can be run. - ANSWER-B
What is a good model fit value?
a. R-squared of .8
b. 99% accurate.
c. 95% accurate.
d. R-squared of p-value minus .05
e. R-squared of .4
f. R-squared of .95
g. Unknowable without knowing/understanding the context and
the domain. - ANSWER-g
Imagine X in the below is a missing value. If I were to run a
median imputer on this set of data what would the returned value
be?
