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Machine Learning 101 Practice Test: Multivariate Linear Regression
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Quiz on multivariate linear regression, gradient descent for multiple variables, and polynomial regression. Multivariate is a controlled or supervised Machine Learning algorithm that analyses multiple data variables. It is a continuation of multiple regression that involves one dependent variable and many independent variables. The output is predicted based on the number of independent variables. Multivariate Multiple Regression is a method of modeling multiple responses, or dependent variables, with a single set of predictor variables. For example, we might want to model both math and... Show more
Machine Learning 101 Practice Test: Multivariate Linear Regression
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25 Questions

1. Gradient descent tries to _____________
2. What is updated by gradient descent after each iteration?
3. What happens when the learning rate is low?
4. Who coined the term regression?
5. There are two features. One is of higher priority. What can be done to improve the hypothesis?
6. x1’s range is 0 to 300. x2’s range is 0 to 1000. What are the suitable ranges of x1 and x2 after feature scaling?
7. A drawback of Polynomial Regression is handling of features with a different priority.
8. h(X) = t0 + t1x + t2x2 + t3x3. What type of regression is this?
9. The learner is trying to predict housing prices based on the size of each house and number of bedrooms. What type of regression is this?
10. What does X(I) represent?
11. Feature scaling can be used to simplify gradient descent for multivariate linear regression.
12. What does xn(i) represent?
13. What is the minimum number of variables required to represent a linear regression model?
14. When was gradient descent invented?
15. The learner is trying to predict the price of a house based on the length and width of the house.x1 = length and x2 = width. What is a better hypothesis?
16. Mean normalization can be used to simplify gradient descent for multivariate linear regression.
17. h(x) = t0 + t1x + t2x2. t0 = 0, t1 = t2 = 1. X is the size of the house. For what value of x, h(x) is minimum?
18. How is the hypothesis represented in multivariate regression? Transpose of matrix a is represented as aT.
19. There is no upper bound on the number of the target variable(s).
20. What is the minimum number of parameters of the gradient descent algorithm?
21. x1’s range is 0 to 300. x2’s range is 0 to 1000. What are the suitable ranges of x1 and x2 after mean normalization?
22. Who introduced the topic of gradient descent?
23. What does (x1(4), x2(4), y(4)) represent or imply?
24. Multivariate linear regression belongs to which category?
25. Let there be n features. What is the dimension of the X vector in hypothesis h(X) = tTX?

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