1
ISYE 6501 MIDTERM 1 EXAM LATEST UPDATES
ACTUAL QUESTIONS AND CORRECT ANSWERS
ALREADY GRADED A+ GUARANTEED SUCCESS
.
What is quantitative data?
Number with a meaning: higher means more, lower means less (e.g., age, sales,
temperature, income)
What is a soft classifier and when is it used?
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 is categorical data?
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
A
What does it mean when the classifier/decision boundary is almost parallel to the
vertical x-axis?
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?
The vertical attribute is all that is needed.
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What is time-series data?
The same data recorded over time often recorded at equal intervals
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
B
What is structured data?
Data that can be stores in a structured way
What is unstructured data?
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
A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___
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.
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.
As lambda drops towards zero
What can SVMs be used for
, 3
to find a classifier with maximum seperation or margin between the two sets of
points?
When to use SVM?
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
What does this formula describe?
Total error
What does this formula describe ?
To maximize the distance between the two lines what do we need to minimize?
m_j > 1
What value do we give for more costly errors
What does this mean in the context of giving a loan?
m_j < 1
Why is it important to scale our data when using SVM?
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
it means the corresponding attribute is probably not relevant
What do kernel methods allow for in SVMs
nonlinear classifiers
What is the common range for scaled data?
between 0 and 1
What is the formula for min-max scaling?
find min and max for a factor
what is common standardization and its formula?
ISYE 6501 MIDTERM 1 EXAM LATEST UPDATES
ACTUAL QUESTIONS AND CORRECT ANSWERS
ALREADY GRADED A+ GUARANTEED SUCCESS
.
What is quantitative data?
Number with a meaning: higher means more, lower means less (e.g., age, sales,
temperature, income)
What is a soft classifier and when is it used?
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 is categorical data?
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
A
What does it mean when the classifier/decision boundary is almost parallel to the
vertical x-axis?
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?
The vertical attribute is all that is needed.
, 2
What is time-series data?
The same data recorded over time often recorded at equal intervals
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
B
What is structured data?
Data that can be stores in a structured way
What is unstructured data?
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
A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___
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.
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.
As lambda drops towards zero
What can SVMs be used for
, 3
to find a classifier with maximum seperation or margin between the two sets of
points?
When to use SVM?
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
What does this formula describe?
Total error
What does this formula describe ?
To maximize the distance between the two lines what do we need to minimize?
m_j > 1
What value do we give for more costly errors
What does this mean in the context of giving a loan?
m_j < 1
Why is it important to scale our data when using SVM?
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
it means the corresponding attribute is probably not relevant
What do kernel methods allow for in SVMs
nonlinear classifiers
What is the common range for scaled data?
between 0 and 1
What is the formula for min-max scaling?
find min and max for a factor
what is common standardization and its formula?
