Hands-On Machine Learning for Algorithmic Trading

The conditional independence assumption

The assumption that is making the model both tractable and justifiably calling it Naive is that the features are independent conditional on the outcome. To illustrate, let's classify an email with the three words Send money now so that Bayes' theorem becomes the following:

Formally, the assumption that the three words are conditionally independent means that the probability of observing send is not affected by the presence of the other terms given the mail is spam; in other words, P(send | money, now, spam) = P(send | spam). As a result, we can simplify the likelihood function:

Using the naive conditional ...