Mean squared error

Mean Squared Error, also known as MSE, is defined as the measure of the average of the squares of the errors. To put it simply, please refer to the following plot:

The dots correlate to data points for our model, while the blue line is the prediction line. The distance between the red dots and the prediction line is the error. For MSE, the value is calculated based on these points and their distances to the line. From that value, the mean is calculated. For MSE, the smaller the value, the better fitting and more accurate predictions you will have with your model.

MSE is best used to evaluate models when outliers are critical to the prediction output.