In previous chapters, we discussed simple, multiple, and polynomial linear regression. The models are special cases of the generalized linear model, a flexible framework that requires fewer assumptions than ordinary linear regression. In this chapter, we will discuss some of these assumptions as they relate to another special case of the generalized linear model called logistic regression.
Unlike the regression models we have previously discussed, logistic regression is used for classification tasks. Recall that the goal in classification tasks is to induce a function that maps an observation to its associated class or label. A learning algorithm must use pairs of feature vectors and their corresponding labels to induce the values of the mapping function's parameters that produce the best classifier, as measured by some performance metric. In binary classification, the classifier must assign instances to one of two classes. In multi...