MBE - Mean Bias Error

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

• Mean Bias Error (MBE): Best possible score is 0.0. Range = (-inf, +inf)

Latex equation code:

\text{MBE}(y, \hat{y}) = \frac{1}{n} \sum_{i=1}^{n}(f_i - y_i)

Example to use MBE 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, decimal=5)
print(evaluator.mean_bias_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, decimal=5)
print(evaluator.MBE(multi_output="raw_values"))