Notes by Lex Toumbourou

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:

1. Calculate error vector as labels - predictions
2. Take the absolute values of the errors
3. 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.

References

• What Are L1 and L2 Loss Functions

Backlinks

• ML Regression
• Root Mean-Squared Error