EVS - Explained Variance Score

• The explained variance score computes the explained variance regression score. If Var is Variance, the square of the standard deviation, then the explained variance is estimated as follow:

\[EVS = 1 - \frac{Var\{ y_{true} - y_{pred} \}}{Var \{ y_{true} \} }\]

• Best possible score is 1.0, greater values are better. Range = (-inf, 1.0]

Latex equation code:

EVS = 1 - \frac{Var\{ y_{true} - y_{pred} \}}{Var \{ y_{true} \} }

Example to use EVS 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.explained_variance_score())

## 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.EVS(multi_output="raw_values"))