ISYE 6501 Midterm 1 | Introduction to Analytics Modeling | Complete Questions & Verified Answers | Georgia Tech
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 does it mean when the classifier/decision The horizontal attribute is all that is needed.
boundary is almost parallel to the vertical x-axis?
What does it mean when the classifier/decision The vertical attribute is all that is needed.
boundary is almost parallel to the horizontal y-axis?
What is time-series data? The same data recorded over time often recorded at equal intervals
What is quantitative data? Number with a meaning: higher means more, lower means less (e.g., age, sales,
temperature, income)
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
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
Which of these is structured data? B
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account
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 A.
size and type of car. Which of these is a data point? A data point is all the information about one observation
A. The 14th person's family size and car type
B. The 14th person's family size
C.The car type of each person
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 As lambda gets larger
importance of a large margin out weights avoiding
mistakes and classifying known data samples.
, ISYE 6501 Midterm 1 | Introduction to Analytics Modeling | Complete Questions & Verified Answers | Georgia Tech
That term also drops towards zero, so the importance As lambda drops towards zero
of minimizing mistakes and classifying known data
points outweighs having a large margin.
What can SVMs be used for 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
Giving a bad loan is twice as costly as withholding a What does this mean in the context of giving a loan?
good loan?
, ISYE 6501 Midterm 1 | Introduction to Analytics Modeling | Complete Questions & Verified Answers | Georgia Tech
m_j < 1 What value do we give for less costly errors?
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 it means the corresponding attribute is probably not relevant
close to zero
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? scaling to a normal distribution with a mean of 0 and standard deviation of 1.
what is the formula for general scaling between b and a
When do you use scaling? Data in a bounded range (e.g., neural networks, RGB values, SAT scores, batting
averages)
When do you use standardization? PCA or clustering
When is KNN used? Used for solving classification problems in which there are more than two
classes.
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 does it mean when the classifier/decision The horizontal attribute is all that is needed.
boundary is almost parallel to the vertical x-axis?
What does it mean when the classifier/decision The vertical attribute is all that is needed.
boundary is almost parallel to the horizontal y-axis?
What is time-series data? The same data recorded over time often recorded at equal intervals
What is quantitative data? Number with a meaning: higher means more, lower means less (e.g., age, sales,
temperature, income)
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
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
Which of these is structured data? B
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account
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 A.
size and type of car. Which of these is a data point? A data point is all the information about one observation
A. The 14th person's family size and car type
B. The 14th person's family size
C.The car type of each person
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 As lambda gets larger
importance of a large margin out weights avoiding
mistakes and classifying known data samples.
, ISYE 6501 Midterm 1 | Introduction to Analytics Modeling | Complete Questions & Verified Answers | Georgia Tech
That term also drops towards zero, so the importance As lambda drops towards zero
of minimizing mistakes and classifying known data
points outweighs having a large margin.
What can SVMs be used for 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
Giving a bad loan is twice as costly as withholding a What does this mean in the context of giving a loan?
good loan?
, ISYE 6501 Midterm 1 | Introduction to Analytics Modeling | Complete Questions & Verified Answers | Georgia Tech
m_j < 1 What value do we give for less costly errors?
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 it means the corresponding attribute is probably not relevant
close to zero
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? scaling to a normal distribution with a mean of 0 and standard deviation of 1.
what is the formula for general scaling between b and a
When do you use scaling? Data in a bounded range (e.g., neural networks, RGB values, SAT scores, batting
averages)
When do you use standardization? PCA or clustering
When is KNN used? Used for solving classification problems in which there are more than two
classes.
