Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is a regression metric that measures the average absolute difference between a model's predicted values and the actual values. It indicates how far predictions are from the correct values, without considering the direction of the error.
What is Mean Absolute Error (MAE)?
MAE is calculated by taking the absolute difference between each predicted and actual value and then averaging those differences. An MAE of 0 indicates perfect predictions. Because errors are measured in the same units as the target variable, MAE is relatively easy to interpret.
Why is Mean Absolute Error (MAE) Important?
MAE provides a straightforward way to evaluate regression model accuracy. Unlike metrics that square errors, MAE gives each error a proportional contribution to the overall score, making it less sensitive to large outliers than Mean Squared Error.
Common use cases
MAE is commonly used to evaluate regression models for demand forecasting, price prediction, time-series forecasting, resource estimation, and other numerical prediction tasks.