ISYE 6501 - Midterm 1
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1. What do descriptive questions ask?: What happened? (e.g., which cus- tomers are most alike)
2. What do predictive questions ask?: What will happen? (e.g., what will Google's stock price be?)
3. W
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hat do prescriptive questions ask?: What action(s) would be best? (e.g., where to put traffic lights)
4. What is a model?: Real-life situation expressed as math.
5. What do classifiers help you do?: differentiate
6. 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.
7. 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.
8. 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.
9. What is time-series data?: The same data recorded over time often recorded at equal intervals
10. What is quantitative data?: Number with a meaning: higher means more, lower means less (e.g., age, sales, temperature, income)
11. What is categorical data?: Numbers w/o meaning (e.g., zip codes), non-nu- meric (e.g., hair color), binary data (e.g., male/female, yes/no, on/off)
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: A
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: B
14. What is structured data?: Data that can be stores in a structured way
15. What is unstructured data?: Data that is not easily described and stored (e.g., written text)
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: A.
A data point is all the information about one observation
17. The farther the wrongly classified point is from the line : The bigger the mistake we've made
18. The term including the margin gets larger so the importance of a large margin out weights avoiding mistakes and classifying known d
: As lambda gets larger
19. That term also drops towards zero, so the importance of minimizing mistakes and classifying known data points outweighs having gin.: As lambda drops towards zero
20. What can SVMs be used for: to find a classifier with maximum seperation or margin between the two sets of points?
21. 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.
22. Error for data point j: What does this formula describe?
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