Mean squared error

Mean squared error is a way to measure how well a line or model fits the data points. When we draw a line or use a model to predict a value, the mean squared error tells us how close the predicted values are to the actual data points. It is calculated by taking the difference between the actual value and the predicted value, squaring the difference (to make it positive, since the differences could be negative or positive), and then taking the average of all these squared differences. A lower mean squared error means the line or model fits the data better.