AE - Absolute Error

\[\text{AE}(y, \hat{y}) = \frac{1}{n} \sum_{i=1}^{n} | \hat{y}_i - y_i |\]

Latex equation code:

\text{AE}(y, \hat{y}) = \frac{1}{n} \sum_{i=1}^{n} | \hat{y}_i - y_i |

• Best possible score is 0.0, smaller value is better. Range = (-inf, +inf)

• Computes the absolute error between two numbers, or for element between a pair of list, tuple or numpy arrays.

Example to use AE 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.single_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.AE())