Why are we always interested in mean-squared distortion?

1. What is the reason to use mean squared error?
2. What is the advantage of MSE?
3. Do we want high or low MSE?
4. Why do we square the difference of the error between predicted values and actual values when calculating mean squared error?

What is the reason to use mean squared error?

MSE is used to check how close estimates or forecasts are to actual values. Lower the MSE, the closer is forecast to actual. This is used as a model evaluation measure for regression models and the lower value indicates a better fit.

What is the advantage of MSE?

The two biggest advantages of MSE or RMSE are that they provide a quadratic loss function and that they are also measures of the uncertainty in forecasting.

Do we want high or low MSE?

There is no correct value for MSE. Simply put, the lower the value the better and 0 means the model is perfect.

Why do we square the difference of the error between predicted values and actual values when calculating mean squared error?

Squaring the difference eliminates the negative values ​​of the difference and ensures that the mean squared error is always greater than or equal to zero.