Mean Squared Error
Mean Squared Error (MSE) measures the average squared difference between estimated values and the actual value. MSE is:
always a zero or positive value
better for values closer to zero
In the diagram below, a Linear Regression line is used to predict Y values based on various X values.
The Mean Squared Error calculates a total error based on the difference between observed and predicted Y values.