AI engineering glossary · ML fundamentals
What is bias-variance tradeoff?
Bias-variance tradeoff: The tension between two sources of prediction error. Simple models make strong assumptions and miss real patterns (high bias); flexible models react to random quirks of the sample and change a lot when data changes (high variance). Good models balance the two so that total error on new data is lowest.
Bias-variance tradeoff: an example
Raising a tree's depth from 2 to 20 lowers bias but raises variance; validation error is typically lowest somewhere in between.
Related terms
More ML fundamentals terms
- Classification
- Concept drift
- Cost-sensitive learning
- Covariate shift
- Cross-validation
- Curse of dimensionality
- Data leakage
- Data snooping
Where you learn it
Bias-variance tradeoff is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.