Naïve Bayes Classifier

Naïve Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naïve” assumption of conditional independence between every pair of features given the value of the class variable. The different naive Bayes classifiers differ mainly by the assumptions they make regarding the distribution of P(x_i|y),  given class variable y and dependent feature vector x₁ through x_n. The list of some naïve Bayes classifiers: Gaussian Naïve Bayes, Multinomial Naïve Bayes, Complement Naïve Bayes, Bernoulli Naïve Bayes, Categorical Naïve Bayes, Out-of-core naïve Bayes model fitting. 

An example: Naïve Bayes classification

Literature:

1. H. Zhang, The optimality of Naive Bayes. Proc. FLAIRS, 2004.
2. Naïve Bayes Classifiers https://www.geeksforgeeks.org/naive-bayes-classifiers/
3. “1.9. Naïve Bayes” https://scikit-learn.org/stable/modules/naive_bayes.html