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Machine Learning 101 Practice Test: Linear Regression
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Machine Learning quiz on linear regression in machine learning, linear regression cost functions, and gradient descent. Linear regression is a statistical method and supervised machine learning algorithm that predicts continuous or quantitative values. It's one of the most widely used algorithms in machine learning problems.  Linear regression uses a linear equation to find the relationship between a dependent variable and one or more independent variables. The model predicts that each increment of a feature that is variable by some fixed amount increases or decreases the predicted output... Show more
Machine Learning 101 Practice Test: Linear Regression
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25 Questions

1. What is the ideal value of t1?
2. Who invented gradient descent?
3. The simplified hypothesis reduces the complexity of the cost function.
4. The independent variable is represented along _________
5. What is the output of gradient descent after each iteration?
6. How to graphically find t1 for which cost function is minimized?
7. The learner is trying to predict housing prices based on the size of each house. The variable “size” is ___________
8. How many variables are required to represent a linear regression model?
9. The hypothesis is given by h(x) = t0 + t1x. t0 gives the value of h(x) when x is 0.
10. What does (x(5), y(5)) represent or imply?
11. The hypothesis is given by h(x) = t0 + t1x. What is the goal of t0 and t1?
12. (x(1), y(1)) = 1, 1.5, (x(2), y(2)) = 2, 3, (x(3), y(3)) = 3, 4.5. Hypothesis: h(x) = t1x, where t1 = 2. How much error is obtained?
13. The hypothesis is given by h(x) = t0 + t1x. What does t1 = 0 after several iterations imply?
14. What is the simplified hypothesis?
15. In a linear regression problem, h(x) is the predicted value of the target variable, y is the actual value of the target variable, m is the number of training examples. What do we try to minimize?
16. Hypothesis h maps from x (independent variable) to y (dependent variable).
17. Cost function has a squared term, but gradient descent does not. Why?
18. (x(1), y(1)) = 1, 1.5, (x(2), y(2)) = 2, 3, (x(3), y(3)) = 3, 4.5. Hypothesis: h(x) = t1x, where t1 = 1.5. How much error is obtained?
19. Learning algorithm outputs the hypothesis.
20. Hypothesis is: h(x) = t0 + t1x. How do we graphically find the desired cost function?
21. What is the generalized goal of gradient descent?
22. The learner is trying to predict the cost of papaya based on its size. The variable “cost” is __________
23. The hypothesis is given by h(x) = t0 + t1x. What are t0 and t1?
24. The cost function contains a summation expression.
25. h(x) = t0 + t1x. Alpha value (learning rate) is 0.1. Initial theta values are 0.3, 0.73. X = [1, 2, 3] and Y = [1, 3, 5]. What is the value of t0 after 1st iteration?