The Naïve Bayes is a classification algorithm based on Bayes' theorem. It is said to be naïve because the foundation of this algorithm is based on naïve assumptions.
- Some of the advantages of the Naïve Bayes Algorithm are:
It is a very simple algorithm for classification problems compared to other classification algorithms.
It is also a powerful algorithm, which implies that it is faster to predict labels using it compared to other classification algorithms.
Another advantage of using it is that it can also give better results on small datasets compared to other algorithms.
Assumption of Naïve Bayes
The Naïve Bayes algorithm is a naïve assumption that implies the condition for independence, of course.
Simply put, one cause is not normally independent of the presence of other causes.
We can consider this very difficult to accept most times, where the probability of a particular feature is strictly correlated with another feature.