Before we get to grips with advanced topics, such as cluster analysis, deep learning, and ensemble models, let's turn our attention to a much simpler model that we have overlooked so far: the Naive Bayes classifier.
Naive Bayes classifiers have their roots in Bayesian inference, named after the famed statistician and philosopher Thomas Bayes (1701-1761). Bayes' theorem famously describes the probability of an event based on prior knowledge of conditions that might lead to the event. We can use Bayes' theorem to build a statistical model that not only can classify data but can also provide us with an estimate of how likely it is that our classification is correct. In our case, we can use Bayesian inference to dismiss an email as spam with high confidence and to determine the probability of a woman having breast cancer...
