# Classification

> [!NOTE]
> **Estimated module length:** 70 minutes

> [!NOTE]
> **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.

> [!CAUTION]
> **Prerequisites:**
>
> This module assumes you are familiar with the concepts covered in the
> following modules:
>
> - [Introduction to Machine Learning](https://developers.google.com/machine-learning/intro-to-ml)
> - [Linear regression](https://developers.google.com/machine-learning/crash-course/linear-regression)
> - [Logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression)

In the [Logistic regression module](https://developers.google.com/machine-learning/crash-course/logistic-regression),
you learned how to use the [**sigmoid function**](https://developers.google.com/machine-learning/glossary#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**](https://developers.google.com/machine-learning/glossary#classification-model) is
the task of predicting which of a set of [**classes**](https://developers.google.com/machine-learning/glossary#class)
(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**](https://developers.google.com/machine-learning/glossary#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**](https://developers.google.com/machine-learning/glossary#multi-class)
problems, which are discussed in more depth later in the course.

> [!IMPORTANT]
> **Key terms:**
>
> - [Binary classification](https://developers.google.com/machine-learning/glossary#binary-classification)
> - [Class](https://developers.google.com/machine-learning/glossary#class)
> - [Classification](https://developers.google.com/machine-learning/glossary#classification-model)
> - [Multi-class classification](https://developers.google.com/machine-learning/glossary#multi-class)
> - [Sigmoid function](https://developers.google.com/machine-learning/glossary#sigmoid-function)

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