Measure

# mean_absolute_error

The mean absolute error (MAE) measures how close the model's predictions are to the actual target values. It is the sum of the absolute value of the difference of each instance prediction and the actual value. For classification, the 0/1-error is used. $$\mathrm{MAE} = \frac{1}{n}\sum_{i=1}^n \left| f_i-y_i\right| =\frac{1}{n}\sum_{i=1}^n \left| e_i \right|.$$ See: http://en.wikipedia.org/wiki/Mean_absolute_error

Source Code:
See WEKA's Evaluation class



## Properties

 Minimum value 0 Maximum value 1 Unit Optimization Lower is better