Mean Absolute Error (MAE) is a statistical measure used to evaluate the accuracy of a model's predictions. It calculates the average of the absolute differences between predicted values and actual values. This means that it takes the errors, disregards their direction (positive or negative), and provides a straightforward way to understand how far off predictions are from reality. MAE is particularly useful in fields like machine learning and forecasting, as it offers a clear metric for assessing model performance. A lower MAE indicates better predictive accuracy, making it easier to compare different models or approaches in terms of their effectiveness.