AI engineering glossary · Classical ML algorithms
What is laplace smoothing?
Laplace smoothing (also called Additive smoothing, Add-one smoothing): Adding a small constant, usually 1, to every count before turning counts into probabilities, so an event never seen in training does not get probability zero. Without it, one unseen word can wipe out a whole naive Bayes product.
Laplace smoothing: an example
If 'cashback' never appeared in non-spam among 1,000 words with vocabulary 5,000, its probability is (0+1)/(1000+5000), not 0.
Related terms
- Naive Bayes
- Multinomial naive Bayes
- Zero-frequency problem
More Classical ML algorithms terms
- Lasso regression
- Lazy learning
- Lift
- LightGBM
- Linear discriminant analysis
- Linear regression
- Linkage criterion
- Local outlier factor
Where you learn it
Laplace smoothing is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.