The Naive Bayes algorithm is a probabilistic learning method. It is known as Naive because it assumes that all events in this word are independent, which is actually quite rare. However, in spite of this assumption, the Naive Bayesian algorithm has proven over time to provide great performance in terms of its prediction accuracy.
The Bayesian probability theory is based on the principle that the estimated likelihood of an event or a potential outcome should be based on the evidence at hand across multiple trials. Bayes’ theorem provides a way to calculate the probability of a given class, given some knowledge about prior observations.
This can be written as follows:
The different elements of this theorem can be explained as follows:
- p(class|observation): This is the probability that the class holds given the observation.
- P(observation): This is the prior probability...
