Naïve Bayes classification

Let's get right into it! Let's begin with Naïve Bayes classification. This machine learning model relies heavily on results from previous chapters, specifically with Bayes theorem:

Let's look a little closer at the specific features of this formula:

• P(H) is the probability of the hypothesis before we observe the data, called the prior probability, or just prior

• P(H|D) is what we want to compute, the probability of the hypothesis after we observe the data, called the posterior

• P(D|H) is the probability of the data under the given hypothesis, called the likelihood

• P(D) is the probability of the data under any hypothesis, called the normalizing constant

Naïve Bayes classification is a classification model, and therefore a supervised model. Given this, what kind of data do we need?

• Labeled data

• Unlabeled data

(Insert jeopardy music here)

If you answered labeled data then you're well on your way to becoming a data scientist!

Suppose we have a data set with n features, (x1,...