Bayes' theorem

In this section, we will first introduce Bayes' theorem and demonstrate how it is applied in ML.

Bayes' theorem calculates the probability of an event given a condition, such that we have prior knowledge about the event, the condition, and the probability of the condition when the event occurs. In our snow prediction example, the event is when snow occurs. A condition would be when the temperature is between 20°F and 32°F. Based on the data, we can calculate the likelihood of temperature being 20°F and 32°F when it snows. Using this data, we can predict the probability of snow given the temperature being between 20°F and 32°F.

Assume that we have a class variable C and a condition variable x. Bayes' theorem is presented in formula 1. We also present a given simple way to remember different components of the algorithm in formula 2.

Formula 1:

Formula 2:

There are four terms that you need to remember from this formula.