Mean absolute error

Mean Absolute Error, also known as MAE, is similar to MSE, with the critical difference that it sums the distances between the points and the prediction lines as opposed to computing the mean. It should be noted, MAE does not take into account directions in calculating the sum. For instance, if you had two data points, equidistant from the line, one being above and the other below, in effect, this would be balanced out with a positive and negative value. In machine learning, this is referred to as mean bias error, however, ML.NET does not provide this as part of the RegressionMetrics class at the time of this writing.

MAE is best used to evaluate models when outliers are considered simply anomalies and shouldn't be counted in evaluating a model's performance.