Naive Bayes
In ML, Naive Bayes (NB) is an example of the probabilistic based on the well-known Bayes' theorem with strong independence assumptions between the features. We will discuss Naive Bayes in detail in this section.
An overview of Bayes' theorem
In probability theory, Bayes' theorem describes the probability of an based on a prior knowledge of conditions that is related to that certain event. This is a theorem of probability originally stated by the Reverend Thomas Bayes. In other words, it can be seen as a way of understanding how the probability theory is true and affected by a new piece of information. For example, if cancer is related to age, the information about age can be used to assess the probability that a person might have cancer more accurately.
Bayes' theorem is stated mathematically as the following equation:
In the preceding equation, A and B are events with P (B) ≠ 0, and the other terms can be described as follows:
- P(A) and P(B) are the probabilities of observing A...
