What is Laplace (additive) smoothing in Naive Bayes?
easyAnswer
- It adds a small constant alpha to every count when estimating so no probability is ever exactly zero.
- Without smoothing, a single word never seen with class c in training would give , killing the posterior.
- Alpha=1 is Laplace smoothing; smaller alpha (e.g., 0.01) is Lidstone smoothing.
- Choose it by cross-validation — it plays the role of a regularizer for the likelihood.
Check yourself — multiple choice
- It smooths the decision boundary
- It adds alpha to counts so is never zero
- It is only for continuous features
- It replaces the priors
Additive smoothing avoids zero probabilities for unseen feature-class combos.
#naive-bayes#regularization
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