VAF - Variance Accounted For

\[\text{VAF}(y, \hat{y}) =\]

• Variance Accounted For between 2 signals (VAF): Best possible score is 100% (identical signal), bigger value is better. Range = (-inf, 100%]

• Link: https://www.dcsc.tudelft.nl/~jwvanwingerden/lti/doc/html/vaf.html

Latex equation code:

\text{VAF}(y, \hat{y}) =

Example to use VAF 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.variance_accounted_for())

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