MSE - Mean Squared Error

\[\text{MSE}(y, \hat{y}) = \frac{\sum_{i=0}^{N - 1} (y_i - \hat{y}_i)^2}{N}\]

Latex equation code:

\text{MSE}(y, \hat{y}) = \frac{\sum_{i=0}^{N - 1} (y_i - \hat{y}_i)^2}{N}

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

• MSE: a risk metric corresponding to the expected value of the squared (quadratic) error or loss.

• Link to equation

Example to use MSE 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_squared_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.MSE(multi_output="raw_values"))