Naive Bayes Classifier

Bayes classification and network

Naive Bayes Classifier-

In machine learning the naive bayes classifier is most useful for finding outcomes with probability technique with use of bayes theorem. The new bayes algorithm are also updated for advanced technique in naive bayes classifier. The new buyes algorithm is established in 1950 with text frequencies. The naive bayes classifier algorithm is the advanced method for calculating future outcomes in Real world. The naive bayes classifier are highly scalable, accurate for predict a value for future. In naive bayes classifier algorithm gives condition dependencies of that item for find particular predict values. That model is easy to build and particularly useful for very large data set along with simplicity and accuracy, It is highly sophisticated for classification method.

The fallowing Equation is used for finding a naive bayes classifier-


In that equation mainly-

P(C/X) is the posterior probability of class.

P(C) is the prior probability of class.

P(X/C) is the likely-hood probability.

P(X) is the prior probability of predication.

The Naive Bayes Classifier gives more accuracy as compare to bayes classifier and bayes network.

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