Machine Learning & AI

Intermediate

Naive Bayes Posterior

Classifies by assuming features are conditionally independent given the class.

Formula

P(y\mid \mathbf{x}) \propto P(y)\prod_i P(x_i\mid y)

Variables

P(y)Class prior

P(x_i\mid y)Feature likelihood

Example

Multiply prior by each feature likelihood

Did You Know?

Its "naive" independence assumption is usually false, yet it works remarkably well for spam filtering.

classification bayesian nlp

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