To understand how the Naive Bayes algorithm works, let’s break it down into its key components:

Probability and Conditional Probability

In Naive Bayes, we deal with probabilities. Each feature or attribute in our data has its own probability, and the algorithm leverages these probabilities to make predictions.

Conditional probability plays a vital role, as it estimates the probability of a particular event given the occurrence of another event.

Naive Assumption

The “naive” in Naive Bayes stems from the assumption that all features in our data are independent of each other. This simplifies the calculations and allows the algorithm to process large datasets quickly.