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+||Brand New Version!
Version 1
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, 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.
,3|Page
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.
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
, 4|Page
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?
50, 60, 70, 80, 100, 60, 5000, X
a. 50
b. 70
c. 80
d. An error
e. 100 - ANSWER-b
The features of the model...
a. Keep the model validation process stable.
b. Are always functions of each other.
c. None of these answers are correct.
d. Are used as proxies for y-hat/y (that is yhat divided by y) - ANSWER-
c
What is the first variable in a decision tree called (before any of the
branches)?
a. Root
QMB3302 UF FALL Final Exam Version 1,2&3
(3 Latest Versions) Newest 2025/2026 With
Complete Questions And Correct Answers
|Already Graded A+||Brand New Version!
Version 1
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, 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.
,3|Page
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.
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
, 4|Page
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?
50, 60, 70, 80, 100, 60, 5000, X
a. 50
b. 70
c. 80
d. An error
e. 100 - ANSWER-b
The features of the model...
a. Keep the model validation process stable.
b. Are always functions of each other.
c. None of these answers are correct.
d. Are used as proxies for y-hat/y (that is yhat divided by y) - ANSWER-
c
What is the first variable in a decision tree called (before any of the
branches)?
a. Root
