Discriminant Analysis
In the previous chapter, we discussed discrete regression models, including classification using logistic regression. In this chapter, we will begin with an overview of probability, expanding into conditional and independent probability. We then discuss how these two approaches to understanding the laws of probability form the basis for Bayes’ Theorem, which is used directly to expand an approach called Bayesian statistics. Following this topic, we dive into Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), two powerful classifiers that model data using the Bayesian approach to probability modeling.
In this chapter we’re going to cover the following main topics:
- Bayes’ Theorem
- LDA
- QDA
