# Chapter 7. Other Advanced Methods

In this chapter, we show off a miscellany of machine learning models
available in R. Even though the main algorithms that we’ve covered thus far
really make up the majority of models, I wanted to include this chapter
to provide a comprehensive view of the machine learning ecosystem in R.

We cover classification again, but through the lens
of Bayesian statistics. This is a popular field of statistics and helps
to transition to some other algorithms that depend on similar logic.
We also cover principal component analysis, support vector machines, and
*k*-nearest neighbor algorithms.

# Naive Bayes Classification

One way to do classification with probabilities is through the use of
*Bayesian statistics*. Although this field can have a rather steep learning
curve, essentially we are trying to answer the question, “Based on the
features we have, what is the probability that the outcome is class
*X*?” A naive Bayes classifier answers this question with a rather
bold assumption: all of the predictors we have are independent of
one another. The advantage to doing this is that we drastically reduce
the complexity of the calculations we’re doing.

## Bayesian Statistics in a Nutshell

Bayesian statistics relies a lot on multiplication of probabilities. Let’s do a quick primer on this so you’re up to speed. Suppose that I ride my bike in 100 races and I win 54 of them (if only!). The probability of me winning a race, therefore, is just the number of times I’ve won divided ...