The naïve bayes classifiers have no built-in way for assessing feature relevance. The conditional and unconditional probabilities associated with the features are determined by Nave Bayes algorithms, which then forecast the class with the highest probability.
The Bayes Theorem is used to create a Naive Bayes classifier. It calculates membership probabilities for each class, such as the likelihood that a certain record or data point belongs to that class. The most likely class is defined as the one having the highest probability.