MACHINE LEARNING

Detailed explanation-1: -Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.

Detailed explanation-2: -Some best examples of the Naive Bayes Algorithm are sentimental analysis, classifying new articles, and spam filtration. Classification algorithms are used for categorizing new observations into predefined classes for the uninitiated data.

Detailed explanation-3: -Spam filtering is a beginner’s example of document classification task which involves classifying an email as spam or non-spam (a.k.a. ham) mail.

Detailed explanation-4: -With Bayes’ Rule, we want to find the probability an email is spam, given it contains certain words. We do this by finding the probability that each word in the email is spam, and then multiply these probabilities together to get the overall email spam metric to be used in classification.