QMB3302 UF FALL Final Exam Version 1,23

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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 supervised learning task is faster and more efficient than an

unsupervised learning task.

• A supervised learning task can handle both numerical and categorical

data, while an unsupervised learning task can only handle numerical data.

• A supervised learning task requires labeled data, while an

unsupervised task does not.

• 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?

• They are easy to interpret.
• They are always the best model to choose. 1 / 4

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• They are the optimal choice of model in a situation where we have

unlabeled data.

• 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)

• Pipelines make it very easy to change small things in your model, like

which variables to include.

• Pipelines help organize the code you used to clean and treat your data.
• Pipelines make it easy to repeat/replicate steps and run multiple

models.

• Pipelines automatically update to new versions of Python.
• 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 combination of XY intercept coordinates.
• estimate or predictions of y. 2 / 4

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• the actual value of y.
• 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)

• The word 'model' instantiates the method and calls the interpreter.

Without this specific word, no model functions are available.

• 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.

• 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?

• R-squared of .8
• 99% accurate.
• 95% accurate.
• R-squared of p-value minus .05
• R-squared of .4
• R-squared of .95 3 / 4

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• 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

• 50
• 70
• 80
• An error
• 100 - ANSWER-b

The features of the model...

• Keep the model validation process stable.
• Are always functions of each other.
• None of these answers are correct.
• 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)?

• Root
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