Naive Bayes


NAIVE BAYES

  • Two events are independent if the occurrence of one event does not affect the probability of the second event
  • If two events are independent then:

P(A∩B)=P(A)×P(B)P(A ∩ B) = P(A) \times P(B)
  • A prior is an additional piece of information that tells us how likely an event is
  • A frequentist approach to statistics does not incorporate a prior
  • A Bayesian approach to statistics incorporates prior knowledge
  • Bayes’ Theorem is the following:

P(A∣B)=P(B∣A)⋅P(A)P(B)P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}

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