Cost Function

While dealing with Linear Regression we can have multiple lines for different values of slopes and intercepts. But the main question that arises is which of those lines actually represents the right relationship between the X and Y and in order to find that we can use the Mean Squared Error or MSE as the parameter. For linear regression, this MSE is nothing but the Cost Function. Mean Squared Error is the sum of the squared differences between the prediction and true value. And the output is a single number representing the cost. So the line with the minimum cost function or MSE represents the relationship between X and Y in the best possible manner.