Introduction to
Naive Bayes
Algorithm
Naïve Bayes algorithm is a supervised learning algorithm, which is based on
Bayes theorem and used for solving classification problems.
It is a probabilistic classifier, which means it predicts on the basis of the
probability of an object.
Some popular examples of Naïve Bayes Algorithm are spam filtration,
Sentimental analysis, and classifying articles.
, Mathematical Formulation
of Naive Bayes
P(A|B) is Posterior probability: Probability of hypothesis A on the observed
event B.
P(B|A) is Likelihood probability: Probability of the evidence given that the
probability of a hypothesis is true.
P(A) is Prior Probability: Probability of hypothesis before observing the
evidence.
P(B) is Marginal Probability: Probability of Evidence.
Naive Bayes
Algorithm
Naïve Bayes algorithm is a supervised learning algorithm, which is based on
Bayes theorem and used for solving classification problems.
It is a probabilistic classifier, which means it predicts on the basis of the
probability of an object.
Some popular examples of Naïve Bayes Algorithm are spam filtration,
Sentimental analysis, and classifying articles.
, Mathematical Formulation
of Naive Bayes
P(A|B) is Posterior probability: Probability of hypothesis A on the observed
event B.
P(B|A) is Likelihood probability: Probability of the evidence given that the
probability of a hypothesis is true.
P(A) is Prior Probability: Probability of hypothesis before observing the
evidence.
P(B) is Marginal Probability: Probability of Evidence.
