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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.

References