What Is Naive Bayes?

Before we build a classifier, let’s talk about the algorithm behind it

Before we begin with the Naive Bayes classifier and its algorithm, first let’s talk about Bayes’ theorem.

Bayes’ Theorem

Bayes’ Theorem gives us the posterior probability of an event given given what is known as prior knowledge.

Prior probability is nothing but the proportion of dependent (binary) variable in the data set. It is the closest guess you can make about a class, without any further information or you can say how probable is A before observing B.

Likelihood is the probability of classifying a given observation as one kind in presence of some other variable. In other words how probable is B when given that A is true or happened.

Marginal likelihood is, how probable is the new datapoint under all the possible variables.

Naive Bayes Classifier is a Supervised Machine Learning Algorithm. It is one of the simple yet effective algorithm. Naive Bayes algorithm classify the object or observation with the help of Bayes Theorem.