Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is a way to measure how close a set of predictions are to the actual outcomes. It calculates the average of the absolute differences between each predicted value and its true value. By taking the absolute difference, MAE ignores whether the prediction was too high or too low, focusing only on how far off it was. A smaller MAE indicates more accurate predictions, while a larger MAE signals less accuracy. It’s a straightforward tool used to assess the accuracy of models in fields like forecasting, machine learning, and statistics.