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Which of the following methods do we use to find the best fit line for data in Linear Regression?Review LaterLeast Square ErrorMaximum LikelihoodLogarithmic LossBoth A and B

Question

Which of the following methods do we use to find the best fit line for data in Linear Regression?Review LaterLeast Square ErrorMaximum LikelihoodLogarithmic LossBoth A and B

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Solution

In Linear Regression, we use the method of Least Square Error to find the best fit line for data. This method minimizes the sum of the squares of the residuals (the differences between the observed and predicted values).

Maximum Likelihood is also a method used in statistics to estimate the parameters of a model. However, it is not typically used in the context of Linear Regression for finding the best fit line.

Logarithmic Loss is a performance metric used in (among other models) logistic regression, neural networks etc. It is not used to find the best fit line in Linear Regression.

So, the correct answer is "Least Square Error".

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