Bayes’s theorem offers the probability of any given event to occur using prior knowledge. In mathematical terms, it can be defined as the true positive rate of the given sample condition divided by the sum of the true positive rate of the said condition and the false positive rate of the entire population.
Two of the most significant applications of Bayes’s theorem in Machine Learning are Bayesian optimization and Bayesian belief networks. This theorem is also the foundation behind the Machine Learning brand that involves the Naive Bayes classifier.