## What is the best fit line in Linear Regression?

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Explain best fit line to define the problem

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## Answers ( 10 )

The best fit line in linear regression is the one which tries to minimize the Residual sum of squares.

It is the line which is supposed to give the best predictions on the unseen data depending on the training

data on which it is built..

Best fit line is the line which minimizes the loss or the line from which the sum of squared distance of data points is the least

A line if best fit is a straight line that is the best approximation of the given set of data.

It is used to study the nature of relation between two variables. ( We are considering the two dimensions case, here )

least square method is one of the method to use determine the best fit.

We use the least squares criterion to pick the regression line. The regression line is sometimes called the “line of best fit” because it is the line that fits best when drawn through the points. It is a line that minimizes the distance of the actual scores from the predicted scores.

The best fit line line in Linear Regression is the line which has the least value of cost function. The cost function in this case would be mean squared error. This line would best fit for predicting the values with the help of data given.

The Best fit line represent the line where the distance between line and data point is minimum. It minimizes the loss.

The best fit line in linear regression is the one which tries to minimize the residual sum of squares or the loss. That means it minimizes the distance of the actual scores from the predicted scores.

The line of best fit, or the equation that represents the data, is found by minimizing the squared distance between the points and the line of best fit, also called the squared error.

In linear regression, the best fit line is the one that seeks to minimize the residual sum of squares.

The best fit line tries to represents the best approximation of a straight line passing through the given set of data points.

It tries to minimize the residual sum of squares or the loss.