In this chapter, we discussed generalized linear models; they extend ordinary linear regression to support response variables with non-normal distributions. Generalized linear models use a link function to relate a linear combination of the explanatory variables to the response variable; unlike ordinary linear regression, the modeled relationship does not need to be linear. In particular, we examined the logistic link function, a sigmoid function that returns a value between 0 and 1 for any real number.
We discussed logistic regression, a generalized linear model that uses the logistic link function to relate explanatory variables to a Bernoulli-distributed response variable. Logistic regression can be used for binary classification, a task in which an instance must be assigned to one of two classes. We used logistic regression to classify spam and ham SMS messages. We then discussed multi-class classification, a task in which each instance must be assigned one label from a set of...