## Mean Absolute Error

Mean Absolute Error (MAE) is a metric for assessing regression predictions. Simply take the average of the absolute error between all labels and predictions in the test set:

$\frac{1}{N}\sum\limits_{j=1}^{n} |y_i - \hat{y}_i|$

Step-by-step:

- Calculate error vector as
**labels**-**predictions** - Take the absolute values of the errors
- Take the mean of all values.

It's also known as L1 Loss because it takes the L1 Norm of the error vector

An alternative to Root Mean-Squared Error.