ISYE 6501 - Midterm 1 with actual
remedy
,What do descriptive questions ask? - correct ans:What happened? (e.g.,
which customers are most alike)
What do predictive questions ask? - correct ans:What will happen? (e.g.,
what will Google's stock price be?)
What do prescriptive questions ask? - correct ans:What action(s) would be
best? (e.g., where to put traffic lights)
What is a model? - correct ans:Real-life situation expressed as math.
What do classifiers help you do? - correct ans:differentiate
What is a soft classifier and when is it used? - correct ans: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? - correct ans: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? - correct ans:The vertical attribute is all that is needed.
What is time-series data? - correct ans:The same data recorded over time
often recorded at equal intervals
What is quantitative data? - correct ans:Number with a meaning: higher
means more, lower means less (e.g., age, sales, temperature, income)
,What is categorical data? - correct ans: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 - correct ans: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 - correct ans:B
What is structured data? - correct ans:Data that can be stores in a structured
way
What is unstructured data? - correct ans: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 - correct ans:A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___ - correct ans: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. -
correct ans: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. - correct
ans:As lambda drops towards zero
What can SVMs be used for - correct ans:to find a classifier with maximum
seperation or margin between the two sets of points?
When to use SVM? - correct ans: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 - correct ans:What does this formula describe?
Total error - correct ans:What does this formula describe ?
To maximize the distance between the two lines what do we need to
minimize? - correct ans:
m_j > 1 - correct ans:What value do we give for more costly errors
Giving a bad loan is twice as costly as withholding a good loan? - correct
ans:What does this mean in the context of giving a loan?
m_j < 1 - correct ans:What value do we give for less costly errors?
remedy
,What do descriptive questions ask? - correct ans:What happened? (e.g.,
which customers are most alike)
What do predictive questions ask? - correct ans:What will happen? (e.g.,
what will Google's stock price be?)
What do prescriptive questions ask? - correct ans:What action(s) would be
best? (e.g., where to put traffic lights)
What is a model? - correct ans:Real-life situation expressed as math.
What do classifiers help you do? - correct ans:differentiate
What is a soft classifier and when is it used? - correct ans: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? - correct ans: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? - correct ans:The vertical attribute is all that is needed.
What is time-series data? - correct ans:The same data recorded over time
often recorded at equal intervals
What is quantitative data? - correct ans:Number with a meaning: higher
means more, lower means less (e.g., age, sales, temperature, income)
,What is categorical data? - correct ans: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 - correct ans: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 - correct ans:B
What is structured data? - correct ans:Data that can be stores in a structured
way
What is unstructured data? - correct ans: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 - correct ans:A.
A data point is all the information about one observation
The farther the wrongly classified point is from the line ___ - correct ans: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. -
correct ans: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. - correct
ans:As lambda drops towards zero
What can SVMs be used for - correct ans:to find a classifier with maximum
seperation or margin between the two sets of points?
When to use SVM? - correct ans: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 - correct ans:What does this formula describe?
Total error - correct ans:What does this formula describe ?
To maximize the distance between the two lines what do we need to
minimize? - correct ans:
m_j > 1 - correct ans:What value do we give for more costly errors
Giving a bad loan is twice as costly as withholding a good loan? - correct
ans:What does this mean in the context of giving a loan?
m_j < 1 - correct ans:What value do we give for less costly errors?
