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This classifier is based on probability which is combined within a Machine Learning model. Hence, it is based on “Bayes Theorem” which states that, we can derive the probability of an event1 given that a retrospective event2 has happened. Here, event2 is the witness and event1 is the hypothesis. The assumption here is that the features are non-dependent which means that the existence of one feature does not affect the other which is why it’s called Naïve. When predictions allocate a continuous value without being discrete, we can ascertain that those values are derived from gaussian distribution. Following is the general formula for Bayes theorem (3.1).

(3.1)

Since our case has a different set or input, our formula for this implementation changes to Equation (3.2).

(3.2)