MRE - Mean Relative Error

\[\text{MRE}(y, \hat{y}) = \frac{1}{N} \sum_{i=0}^{N - 1} \frac{|y_i - \hat{y}_i|}{|y_i|}\]

Latex equation code:

\text{MRE}(y, \hat{y}) = \frac{1}{N} \sum_{i=0}^{N - 1} \frac{|y_i - \hat{y}_i|}{|y_i|}}

• Mean Relative Error (MRE) or Mean Relative Bias (MRB)

• Best possible score is 0.0, smaller value is better. Range = [0, +inf)

Example to use MRE 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_relative_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.MRE(multi_output="raw_values"))