Mean Squared Error

Mean Absolute Error (MAE) quantifies the average magnitude of errors between predicted and actual values in a dataset. It sums all absolute differences without considering their signs and divides by the number of observations. MAE provides an intuitive measure of prediction accuracy, highlighting how close forecasts are to real outcomes, with equal emphasis on all error sizes regardless of whether predictions are above or below true values.