MPE - Mean Percentage Error
\[\text{MPE}(y, \hat{y}) = \frac{100\%}{N} \sum_{i=0}^{N - 1} \frac{y_i - \hat{y}_i}{y_i}.\]
Latex equation code:
\text{MPE}(y, \hat{y}) = \frac{100\%}{N} \sum_{i=0}^{N - 1} \frac{y_i - \hat{y}_i}{y_i}.
Mean Percentage Error (MPE): Best possible score is 0.0. Range = (-inf, +inf)
Example to use MPE 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_percentage_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.MPE(multi_output="raw_values"))