training deep leaning models predicted values the end result is positive numberas we take square of the disagreement essentiallythe square operation is valuable because larger differences are penalized more in use cases where you wouldn' want the model to penalize large difference more heavilymse would not be the ideal choice the lower the mse for given modelthe better the performance for the model mathematicallywe can define mse as mse yi ^ = yrmse = mean absolute error the mean absolute error (maecomputes the mean of the absolute difference between predictions and target the outcomewhich is always positiveis much more interpretable performance metric than mse for regression use cases the lower the mae for model the better the performance mathematicallywe can define mae as mae yi ^ = mean absolute percentage error the mean absolute percentage error (mapeis the percentage equivalent of the mae given its relative natureit is by far the most interpretable performance metric for regression the lower the mape for modelthe better the performance for the model