What is Mean Absolute Error (MAE)?

In the context of machine learning, absolute error refers to the magnitude of difference between the prediction of an observation and the true value of that observation. MAE takes the average of absolute errors for a group of predictions and observations as a measurement of the magnitude of errors for the entire group. MAE can also be referred as L1 loss function.


Why is Mean Absolute Error Important?

As one of the most commonly used loss functions for regression problems, MAE helps users to formulate learning problems into optimization problems. It also serves as an easy-to-understand quantifiable measurement of errors for regression problems.


How C3.ai Helps Organizations Use Mean Absolute Error

The C3 AI® Suite offers mean absolute error, also known as L1 loss function, as a ready-to-use MLScoringMetric that is well-integrated with other C3 ML-related functionalities such as model training and model tuning.