Supervised learning using Naïve Bayes
Naïve Bayes is one of most famous machine learning algorithms to date. It is widely used in text classification techniques.
Naïve Bayes methods come under the set of supervised learning algorithms. It is a probabilistic classifier and is based on Bayes' theorem. It takes the "naïve" assumption that every pair of features is independent of one another.
And in spite of these assumptions, Naïve Bayes classifiers work really well. Their most famous use case is spam filtering. The effectiveness of this algorithm is justified by the requirement of quite a small amount of training data for estimating the required parameters.
These classifiers and learners are quite fast when compared to other methods.
In this given formula:
A and B are events.
P(A) and P(B) are probabilities of A and B.
These are prior probabilities and are independent of each other.
P(A | B) is the probability of A with the condition that B is true. It is the posterior probability of class (A...
