RE - Relative Error

\[\text{RE}(y, \hat{y}) = \frac{|y_i - \hat{y}_i|}{|y_i|}\]

Latex equation code:

\text{RE}(y, \hat{y}) = \frac{|y_i - \hat{y}_i|}{|y_i|}

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

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

  • The Relative Error (RE) is a metric used to evaluate the accuracy of a regression model by measuring the ratio of the absolute error to the actual value.

Example to use RE 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_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.RE())