ISYE 6501 - MIDTERM 1 EXAM 2024/2025 WITH 100% ACCURATE SOLUTIONS
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Course
ISYE 6501
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ISYE 6501
What do descriptive questions ask?-Answer What happened? (e.g., which customers are most alike)
What do predictive questions ask?-Answer What will happen? (e.g., what will Google's stock price be?)
What do prescriptive questions ask?-Answer What action(s) would be best? (e.g., where to put tr...
ISYE 6501 - MIDTERM 1 EXAM 2024/2025 WITH 100%
ACCURATE SOLUTIONS
What do descriptive questions ask?-Answer✔✔ What happened? (e.g.,
which customers are most alike)
What do predictive questions ask?-Answer✔✔ What will happen? (e.g.,
what will Google's stock price be?)
What do prescriptive questions ask?-Answer✔✔ What action(s) would
be best? (e.g., where to put traffic lights)
What is a model?-Answer✔✔ Real-life situation expressed as math.
What do classifiers help you do?-Answer✔✔ differentiate
What is a soft classifier and when is it used?-Answer✔✔ In some cases,
there won't be a line that separates all of the labeled examples. So we use
a classifier that minimizes the number of mistakes.
What does it mean when the classifier/decision boundary is almost
parallel to the vertical x-axis?-Answer✔✔ The horizontal attribute is all
that is needed.
,What does it mean when the classifier/decision boundary is almost
parallel to the horizontal y-axis?-Answer✔✔ The vertical attribute is all
that is needed.
What is time-series data?-Answer✔✔ The same data recorded over time
often recorded at equal intervals
What is quantitative data?-Answer✔✔ Number with a meaning: higher
means more, lower means less (e.g., age, sales, temperature, income)
What is categorical data?-Answer✔✔ Numbers w/o meaning (e.g., zip
codes), non-numeric (e.g., hair color), binary data (e.g., male/female,
yes/no, on/off)
Which of these is time series data?
A. The average cost of a house in the United States every year since
1820
B. The height of each professional basketball player in the NBA at the
start of the season-Answer✔✔ A
Which of these is structured data?
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account-Answer✔✔ B
What is structured data?-Answer✔✔ Data that can be stores in a
structured way
,What is unstructured data?-Answer✔✔ Data that is not easily described
and stored (e.g., written text)
A survey of 25 people recorded each person's family size and type of
car. Which of these is a data point?
A. The 14th person's family size and car type
B. The 14th person's family size
C.The car type of each person-Answer✔✔ A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___-Answer✔✔
The bigger the mistake we've made
The term including the margin gets larger so the importance of a large
margin out weights avoiding mistakes and classifying known data
samples.-Answer✔✔ As lambda gets larger
That term also drops towards zero, so the importance of minimizing
mistakes and classifying known data points outweighs having a large
margin.-Answer✔✔ As lambda drops towards zero
What can SVMs be used for-Answer✔✔ to find a classifier with
maximum seperation or margin between the two sets of points?
, When to use SVM?-Answer✔✔ If it's impossible to avoid classification
errors, SVM can find a classifier that trades off reducing errors and
enlarging the margin.
Error for data point j-Answer✔✔ What does this formula describe?
Total error-Answer✔✔ What does this formula describe ?
To maximize the distance between the two lines what do we need to
minimize?-Answer✔✔
m_j > 1-Answer✔✔ What value do we give for more costly errors
Giving a bad loan is twice as costly as withholding a good loan?-
Answer✔✔ What does this mean in the context of giving a loan?
m_j < 1-Answer✔✔ What value do we give for less costly errors?
Why is it important to scale our data when using SVM?-Answer✔✔
We're looking to minimize the sum of the squares of the coefficients, but
if our data has very different scales a small change in one could swamp a
huge change in the other.
what does it signify when a coefficient for a classifier is close to zero-
Answer✔✔ it means the corresponding attribute is probably not relevant
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