Exploring Naïve Bayes
The Naïve Bayes classifier belongs to the family of probabilistic classifiers. It computes the probabilities of each predictive feature (also referred to as an attribute or signal) of the data belonging to each class in order to make a prediction of the probability distribution over all classes. Of course, from the resulting probability distribution, we can conclude the most likely class that the data sample is associated with. What Naïve Bayes does specifically, as its name indicates, is as follows:
- Bayes: As in, it maps the probability of observed input features given a possible class to the probability of the class given observed pieces of evidence based on Bayes’ theorem.
- Naïve: As in, it simplifies probability computation by assuming that predictive features are mutually independent.
I will explain Bayes’ theorem with examples in the next section.
Bayes’ theorem by example
It is important...
