How does Bayes' theorem drive Naive Bayes classification?
easyAnswer
- For class c and features x, ∝ .
- Naive Bayes assumes features are conditionally independent given the class: .
- This lets you estimate each separately (very fast, needs little data per parameter).
- At prediction time you pick the class with the largest posterior score.
- Priors P(c) are usually class frequencies; likelihoods come from a chosen distribution (Gaussian, Multinomial, Bernoulli).
Check yourself — multiple choice
- It maximizes likelihood, ignoring priors
- ∝ with conditional independence of features given the class
- It requires features to be Gaussian
- It cannot handle multi-class
Bayes' rule + conditional feature independence given the class.
#naive-bayes#classification#theory
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