RAE - Relative Absolute Error
\[\text{RAE}(y, \hat{y}) = \frac{\Big[\sum_{i=1}^{n}(\hat{y}_i - y_i)^2\Big]^{1/2}}{\Big[\sum_{i=1}^{n}(y_i)^2\Big]^{1/2}}\]
Latex equation code:
\text{RAE}(y, \hat{y}) = \frac{\Big[\sum_{i=1}^{n}(\hat{y}_i - y_i)^2\Big]^{1/2}}{\Big[\sum_{i=1}^{n}(y_i)^2\Big]^{1/2}}
Relative Absolute Error (RAE): Best possible score is 0.0, smaller value is better. Range = [0, +inf)
The Relative Absolute Error (RAE) is a metric used to evaluate the accuracy of a regression model by measuring the ratio of the mean absolute error to the
mean absolute deviation of the actual values.
Example to use RAE 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.relative_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.RAE(multi_output="raw_values"))