Naive Bayes Classifiers

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.

Supervised Machine Learning

In supervised machine learning you use the terms features and labels.
Features could for instance be the bumpiness and slope of the ground as identified by an autonomous car, and the label could be whether or not the driver drives slow of fast on it. If you then would add a new feature you could predict wether or not the user would drive fast or slow on it.