MSE is short for mean squared error.
It measures the average squared difference between the estimated values and the actual value.
The mean squared error tells you how close a regression line is to a set of points.
Formula to calculate MSE.
Example:
Suppose you were measuring the length of 5 strings, calculate the MSE if the sum of the observed value is 60 cm and the sum of the predicted value is 61.5 cm.
Therefore, the MSE is 0.45.
