So far, the focus of this chapter has been on the binary classification task where we have two classes. We'll now turn to the problem of multiclass prediction. In Chapter 1, Gearing Up for Predictive Modeling, we studied the iris data set, where the goal is to distinguish between three different species of iris, based on features that describe the external appearance of iris flower samples. Before presenting additional examples of multiclass problems, we'll state an important caveat. The caveat is that several other methods for classification that we will study in this book, such as neural networks and decision trees, are both more natural and more commonly used than logistic regression for classification problems involving more than two classes. With that in mind, we'll turn to multinomial logistic regression, our first extension of the binary logistic classifier.