MAE - Mean Absolute Error
Mean Absolute Error (MAE) [2] is a statistical measure used to evaluate the accuracy of a forecasting model, such as a regression model or a time series model. It measures the average magnitude of the errors between the predicted values and the actual values in the units of the response variable. The MAE is calculated as the average of the absolute differences between the predicted values and the actual values. In other words, it is the mean of the absolute errors. Best possible score is 0.0, smaller value is better. Range = [0, +inf)
The MAE is a widely used measure of forecast accuracy because it is easy to understand and interpret. A lower MAE indicates better forecast accuracy. However, like the RMSE, the MAE is not normalized and is dependent on the scale of the response variable, making it difficult to compare the MAE values across different datasets with different scales.
Example to use MAE metric:
from numpy import array
from permetrics.regression import RegressionMetric
## For 1-D array
y_true = array([3, -0.5, 2, 7])
y_pred = array([2.5, 0.0, 2, 8])
evaluator = RegressionMetric(y_true, y_pred)
print(evaluator.mean_absolute_error())
## For > 1-D array
y_true = array([[0.5, 1], [-1, 1], [7, -6]])
y_pred = array([[0, 2], [-1, 2], [8, -5]])
evaluator = RegressionMetric(y_true, y_pred)
print(evaluator.MAE(multi_output="raw_values"))