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. What 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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