Explain The Naïve Bayesian Classifier with example.

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 The Naïve Bayesian Classifier

Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. It is also known as a simple Bayesian classifier.




 The main advantage of the Naïve Bayes classifier is it is easy to implement and gives good results in most cases. However, the Naive Bayes classifier assumes that class conditional independence that is features are independent of each other, such assumptions result in the loss of accuracy of the classifier. Because, practically, dependencies exist among features, for example, fever, and cough. Such dependencies among features cannot be modeled by Naïve Bayesian Classifier.

Working of  The Naïve Bayesian Classifier


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