The linear regression model discussed in Chapter 3 assumes that the response variable is quantitative.
In many situations, the response variable is instead qualitative.In this chapter, we study approaches for predicting qualitative responses, a process that is known as classification.
Often the methods used for classification first predict the probability of each of the categories of a qualitative variable, as the basis for making the classification. In this sense they also behave like regression methods.
There are many possible classification techniques, or classifiers, that one might use to predict a qualitative response. In this chapter, we discuss three of the most widely-used classifiers: logistic regression, linear discriminant analysis, and K-nearest neighbors.
We discuss more compute-intensive methods in later chapters, such as generalized additive models(ch7), trees, random forests, and boosting(ch8), and support vector machines(ch9).