In this chapter, we learned about Naive Bayes and how it can be applied in different ways for different purposes.
Bayes' theorem states the following:
Here, P(A|B) is the conditional probability of A being true, given that B is true. It is used to update the value of the probability that A is true given the new observations about other probabilistic events. This theorem can be extended to a statement with multiple random variables:
The random variables B1,...,Bn have to be conditionally independent given A. The random variables can be discrete or continuous and follow a probability distribution, for example, normal (Gaussian) distribution.
We also studied the discrete random variable. We learned that it is best to ensure that you have a data item for each value of a discrete random variable given any...
