Mean Absolute Error

Mean Absolute Error (MAE) is a metric used to measure the accuracy of a model's predictions. It calculates the average absolute differences between predicted values and actual values, providing a straightforward interpretation of prediction error. MAE is particularly useful because it treats all errors equally, making it less sensitive to outliers compared to other metrics like Mean Squared Error. This makes it a popular choice in various applications, including regression analysis and forecasting, where understanding the average error magnitude is crucial for model evaluation.