In the Logistic regression module, you learned how to use the sigmoid function to convert raw model output to a value between 0 and 1 to make probabilistic predictions—for example, predicting that a given email has a 75% chance of being spam. But what if your goal is not to output probability but a category—for example, predicting whether a given email is "spam" or "not spam"?
Classification is the task of predicting which of a set of classes (categories) an example belongs to. In this module, you'll learn how to convert a logistic regression model that predicts a probability into a binary classification model that predicts one of two classes. You'll also learn how to choose and calculate appropriate metrics to evaluate the quality of a classification model's predictions. Finally, you'll get a brief introduction to multi-class classification problems, which are discussed in more depth later in the course.