3.3.4. Mean Absolute Error

The mean absolute error (MAE) is a measure of the error between a pair of random variables expressing the same event. It is computed as

$$MAE = \frac{1}{n} \sum\_{i=1}^{n} |P\_i - A\_i| \tag{13}$$

Following (13), it can be observed that an errorless model will generate a zero MAE value, since *Pi* = *Aj*, thus indicating that the MAE ranges from 0 to infinity, with 0 being an ideal model. For this reason, the MAE is a boundless metric and thus, is data specific. Nevertheless, it remains a valuable metric for comparing models that are based on the same input data.
