Which of the following methods do we use to find the best fit line for data in Linear Regression?

Solution(By Examveda Team)

In Linear Regression, the method used to find the best fit line for data is Option A: Least Square Error. This technique minimizes the sum of the squared differences (errors) between the predicted values and the actual values in the dataset. The goal is to find the line that minimizes the overall error, making it the "best fit" line for the data.

Option B: Maximum Likelihood is not the primary method for finding the best fit line in Linear Regression. Maximum Likelihood is a statistical method used in other modeling techniques, but it is not the standard approach in Linear Regression.

Option C: Logarithmic Loss is typically associated with logistic regression and classification problems, not Linear Regression.

Option D: Both A and B suggests that both Least Square Error and Maximum Likelihood are used to find the best fit line in Linear Regression. While Maximum Likelihood can be applied in some cases, the primary and standard method is Least Square Error.

Therefore, the correct method for finding the best fit line in Linear Regression is Option A: Least Square Error.