Question: 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?

Question: 2

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

Question: 3

You can compute the residual by-

Question: 4

How to see the value of residuals geometrically

Question: 5

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

Question: 6

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

Question: 7

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

Question: 8

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

Question: 9

Can we perform linear regression with a neural network?

Question: 10

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

Question: 11

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

Question: 12

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

Question: 13

What is a support vector?

Question: 14

What is a kernel?

Question: 15

Which of the following is not a kernel?

Question: 16

What does epsilon represent in Support Vector Regression?

Question: 17

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

Question: 18

Which one is a different algorithm?

Question: 19

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

Question: 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)

Question: 21

Which of the following is a regression algorithm?

Question: 22

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

Question: 23

Regression is a-

Question: 24

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

Question: 25

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

Question: 26

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

Question: 27

If you fit 2 degree polynomial in linear regression-

Question: 28

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

Question: 29

Linear regression is-

Question: 30

What is true about Residuals?