Performing naïve Bayes classification

Let’s get right into it! Let’s begin with naïve Bayes classification. This ML 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 or unlabeled data?

(Insert Jeopardy music here)

If you answered labeled data, then...