#### Regression Quiz Questions

1. Suppose you have to predict the salary of an employee from their years of experience where the dataset has a salary range from 10000 to 50000. In which of the intervals your regressive model should predict?

2. In simple linear regression, if you change the input value by 1 then output value will be changed by:

3. You can compute the residual by-

4. How to see the value of residuals geometrically

5. The equation of the regression line is y = 5x + 3. Predict y when x = 8.

6. The equation of the regression line is y = 8x - 2. Compute the residual for the point (4, 28)

7. What would be the best regression model for more than one independent variable?

8. Suppose you have observed that you data has an exponential growth tendency. Then what regression model you should use-

9. Can we perform linear regression with a neural network?

10. If you get a poor accuracy using a simple linear regression model. What will be the cause behind it-

11. If your data grows in a non-linear fashion. Which model won’t perform well?

12. Suppose you got a training accuracy of 90% and a test accuracy of 50%. What happened with your model-

13. What is a support vector?

14. What is a kernel?

15. Which of the following is not a kernel?

16. What does epsilon represent in Support Vector Regression?

17. In Regression, a decision tree splits the dataset based on-

18. Which one is a different algorithm?

19. Which one is not a better algorithm in the sense of overfitting?

20. If the actual value of a data point is 50 and the predicted value is 55, what will be the Mean Absolute Error(MAE)

21. Which of the following is a regression algorithm?

22. Suppose you have to predict the salary of employees from their experience. This is a-

23. Regression is a-

24. Which of the following is/are true about Normal Equation?

25. Which methods are used to find the best fit line in linear regression?

26. What will happen when you increase the size of training data?

27. If you fit 2 degree polynomial in linear regression-

28. Which of the following evaluation metrics can be used for Regression?

29. Linear regression is-

30. What is true about Residuals?