What Is Mean Absolute Error (MAE) In Machine Learning?

Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is a regressive loss measure looking at the absolute value difference between a model’s predictions and ground truth, averaged out across the dataset. Unlike MSE, MAE is weighted on a linear scale and therefore doesn’t put as much weight on outliers. This provides a more even measure of performance, but means large errors and smaller errors are weighted the same. Something to consider depending on your specific model use case.