If we were predicting a numerical quantity, such as the temperature at noon tomorrow in a particular place, the accuracy would usually be summarized by the error – the difference between the observed and predicted temperature. The usual summary of the error over a number of days is the mean-squared-error (MSE) – this is the average of the squares of the errors, and is analogous to the least-squares criterion we saw used in regression analysis.
