AI engineering glossary · ML fundamentals
What is BIC (bayesian information criterion)?
Bayesian information criterion (also called BIC, Schwarz criterion): A model comparison score similar to the Akaike criterion but with a heavier penalty on complexity for larger datasets: BIC = k ln(n) - 2 ln(L), where n is the number of observations. It tends to favour simpler models and lower values are better.
Bayesian information criterion: an example
With 1,000 rows, BIC punishes each extra parameter by about 6.9 units, so a marginal feature is often not worth including.
Related terms
More ML fundamentals terms
- Bias error
- Bias-variance tradeoff
- Classification
- Concept drift
- Cost-sensitive learning
- Covariate shift
- Cross-validation
- Curse of dimensionality
Where you learn it
Bayesian information criterion is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.