Laplace smoothing (also known as additive smoothing) is a very particular smoothing technique concerning categorical data. Laplace smoothing is almost always associated with a probability regularisation task and thus it does not immediately relate to "standard" smoothing techniques (eg. kernel smoothing, smoothing spline, etc.) concerned with variations along a (usually linear) continuum.
I think it will be beneficial to have a separate
[laplace-smoothing] tag. At first instance it seems that 25 to 30 questions might warrant the tag. I think it will make it easier for ML practitioners to recognise/filter relevant questions. Some questions that would affected are the following:
- Understanding Add-1/Laplace smoothing with bigrams
- Laplace smoothing and Dirichlet prior
- Laplace smoothing and naive bayes
- Laplace smoothing understanding implementation
- Markov chain getting stuck due to insufficient data samples
- What's a good approach to estimate the probability of word frequencies?
- How to handle unseen features in a Naive Bayes classifier?
- In Naive Bayes, why bother with Laplacian smoothing when we have unknown words in the test set?
(Unsurprisingly, NLP applications seems to generally concerned with this type of regularisation...)