ISYE 6501 - FINAL EXAM Questions with Correct Solutions
Question:
1. What do descriptive questions ask?
Answer:
What happened? (e.g., which customers are most alike)
Question:
2. What do predictive questions ask?
Answer:
What will happen? (e.g., what will Google's stock price be?)
Question:
3. What do prescriptive questions ask?
Answer:
What action(s) would be best? (e.g., where to put traffic lights)
Question:
4. What is a model?
Answer:
Real-life situation expressed as math.
Question:
5. What do classifiers help you do?
Answer:
differentiate
Question:
6. 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.
Question:
7. 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.
Question:
8. 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.
Question:
9. What is time-series data?
Answer:
The same data recorded over time often recorded at equal intervals
Question:
10. What is quantitative data?
Answer:
Number with a meaning: higher means more, lower means less (e.g., age, sales, temperature,
income)
Question:
11. 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)
Question:
12. 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
Question:
13. 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
,Question:
14. What is structured data?
Answer:
Data that can be stores in a structured way
Question:
15. What is unstructured data?
Answer:
Data that is not easily described and stored (e.g., written text)
Question:
16. 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
Question:
17. The farther the wrongly classified point is from the line ___
Answer:
The bigger the mistake we've made
Question:
18. 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
Question:
19. 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
, Question:
20. What can SVMs be used for
Answer:
to find a classifier with maximum seperation or margin between the two sets of points?
Question:
21. 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.
Question:
22. What does this formula describe?
Answer:
Error for data point j
Question:
23. What does this formula describe ?
Answer:
Total error
Question:
24. To maximize the distance between the two lines what do we need to minimize?
Answer:
Question:
25. m_j > 1
Answer:
What value do we give for more costly errors
Question:
1. What do descriptive questions ask?
Answer:
What happened? (e.g., which customers are most alike)
Question:
2. What do predictive questions ask?
Answer:
What will happen? (e.g., what will Google's stock price be?)
Question:
3. What do prescriptive questions ask?
Answer:
What action(s) would be best? (e.g., where to put traffic lights)
Question:
4. What is a model?
Answer:
Real-life situation expressed as math.
Question:
5. What do classifiers help you do?
Answer:
differentiate
Question:
6. 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.
Question:
7. 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.
Question:
8. 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.
Question:
9. What is time-series data?
Answer:
The same data recorded over time often recorded at equal intervals
Question:
10. What is quantitative data?
Answer:
Number with a meaning: higher means more, lower means less (e.g., age, sales, temperature,
income)
Question:
11. 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)
Question:
12. 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
Question:
13. 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
,Question:
14. What is structured data?
Answer:
Data that can be stores in a structured way
Question:
15. What is unstructured data?
Answer:
Data that is not easily described and stored (e.g., written text)
Question:
16. 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
Question:
17. The farther the wrongly classified point is from the line ___
Answer:
The bigger the mistake we've made
Question:
18. 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
Question:
19. 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
, Question:
20. What can SVMs be used for
Answer:
to find a classifier with maximum seperation or margin between the two sets of points?
Question:
21. 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.
Question:
22. What does this formula describe?
Answer:
Error for data point j
Question:
23. What does this formula describe ?
Answer:
Total error
Question:
24. To maximize the distance between the two lines what do we need to minimize?
Answer:
Question:
25. m_j > 1
Answer:
What value do we give for more costly errors
