Question 1

What is linear regression in machine learning?

Accepted Answer

Linear regression is a supervised learning algorithm used to model the relationship between a dependent variable and one or more independent variables by making a linear equation.

Question 2

How does linear regression differ from logistic regression?

Accepted Answer

Linear regression basically predicts continuous numerical values, while logistic regression predicts binary outcomes  in the term of yes/no or 0/1.

Question 3

How is the accuracy of a linear regression model evaluated?

Accepted Answer

The accuracy of a linear regression model is evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).

Question 4

What is the impact of outliers on linear regression?

Accepted Answer

Outliers can pull the line towards them, making the slope and intercept of the line change. This can cause the model to make predictions that are not accurate. In order to make model predictions more accurate and reliable it is important to deal with outliers properly.

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