Welcome to our exploration tour of the Naive Bayes Classifier! This robust classification algorithm is renowned for its simplicity and effectiveness. We will implement it from scratch in Python, allowing you to leverage its sheer power without the need for any prebuilt libraries. Let's get started!

Let's do a quick recall of probability theory.

$P(A)$ usually denotes the likelihood of a certain event A occurring. $P(A|B)$, on the other hand, indicates the probability of event A taking place, assuming event B has already happened.

For instance, let's imagine there's a bag housing three marbles - one red and two blue. Denote A as the event where a red marble is picked, and B when a blue one is drawn. The probability of A, , is in this case.