Naive Bayes Classifier is a probabilistic classifier used in supervised machine learning that is especially useful when categorizing texts. They apply Bayes’ Theorem which describes the probability of an event to take place based on given knowledge of conditions related to the event.

Where A and B are events and A != B

P(A) and P(B) are probabilities of observing A and B without regards to each other.

P(A|B), a conditional probability of observing A given that B is true.

P(B|A) is the probability of observing B if A is true.

One example of a library where you can use a Gaussian Naive Bayes classifer can be found in the scikit-learn python library sklearn.naive_bayes.GaussianNB with which you can train (fit) to tell wether a feature match a label.