Classification

Estimated module length: 70 minutes

Learning objectives

• Determine an appropriate threshold for a binary classification model.
• Calculate and choose appropriate metrics to evaluate a binary classification model.
• Interpret ROC and AUC.

Prerequisites:

This module assumes you are familiar with the concepts covered in the following modules:

• Introduction to Machine Learning
• Linear regression
• Logistic regression

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 predict 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.

Key terms:

• Binary classification
• Class
• Classification
• Multi-class classification
• Sigmoid function

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