Mastering Python for Finance - Second Edition

Mean squared error as a risk metric

Like the MAE, the mean squared error (MSE) is a risk metric that measures the average of the squares of the prediction errors and can be written as follows:

Squaring the errors means that values of MSE are always positive, and low values of MSE are highly desired. A perfect MSE score of 0 implies that our prediction powers are exactly aligned with actual values, and that the squares of such differences are negligible. While the application of both the MSE and MAE helps determine the strength of our model's predictive powers, MSE triumphs over MAE by penalizing errors that are farther away from the mean. ...