AI engineering glossary · Evaluation metrics
What is explained variance score?
Explained variance score: A regression metric equal to 1 - Var(y - y_hat) / Var(y): the fraction of the target's variance that the model's predictions account for. A value of 1 is perfect. Unlike R-squared it ignores a constant offset in the errors, so a biased model can still score well.
Explained variance score: an example
If the target's variance is 100 and the variance of the model's errors is 20, the explained variance score is 0.8.
Related terms
More Evaluation metrics terms
- F-beta score
- F1 score
- False negative
- False negative rate
- False positive
- False positive rate
- Fleiss' kappa
- G-mean
Where you learn it
Explained variance score is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.