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Mean Squared Error is a performance metric applied to a regression problem and it is a measure of errors between predicted outcomes versus correct, true outcomes. For any couple (predicted outcome, true value of the outcome) the error is the difference between the two elements, i.e. predicted outcome – true value of the outcome. MSE is computed as the average of the squares of the errors, i.e. the average squared difference between the estimated values and the actual values.