AI engineering glossary · Evaluation metrics
What is MBE (mean bias error)?
Mean bias error (also called MBE, bias in forecast): The average signed prediction error, with predictions minus actuals: (1/N) * sum of (y_hat - y). Unlike absolute-error metrics, positive and negative errors cancel, so it reveals whether a model systematically over- or under-predicts.
Mean bias error: an example
Signed errors +5, -2, +2, +20 average to +6.25, showing the model tends to over-predict prices by about 6 lakh.
Related terms
More Evaluation metrics terms
- Mean reciprocal rank
- Mean squared error
- Mean squared logarithmic error
- Median absolute error
- METEOR
- Micro averaging
- Negative predictive value
- Normalised discounted cumulative gain
Where you learn it
Mean bias error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.