Naive Bayes is a Supervised Machine Learning algorithm based on the Bayes Theorem that is used to solve classification problems by following a probabilistic approach. It is based on the idea that the predictor variables in a Machine Learning model are independent of each other. Meaning that the outcome of a model depends on a set of independent variables that have nothing to do with each other.
Naive Bayes is one of the simplest algorithms available and yet it works pretty well most of the time. It’s almost never the best solution but it’s typically good enough to give you an idea of whether you can get a job done.