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.

MAE graphic

Sign up for our monthly newsletter, The Evaluator.

Sign up now