Mean Absolute Error
Mean Absolute Error (MAE) is a statistical metric used to measure the average magnitude of errors between predicted and actual values in regression models, without considering their direction. It calculates the absolute differences between predictions and observations, then averages them to provide a straightforward interpretation of model accuracy. MAE is widely used in machine learning, forecasting, and data analysis to evaluate predictive performance.
Developers should learn MAE when building or evaluating regression models, as it offers a robust and interpretable measure of prediction error that is less sensitive to outliers compared to metrics like Mean Squared Error. It is particularly useful in applications where all errors are equally important, such as demand forecasting, financial modeling, or any scenario where understanding average error magnitude is critical for decision-making.