# Logistic Regression

> [!NOTE]
> **Estimated module length:** 35 minutes

> [!NOTE]
> **Learning Objectives**
>
> - Identify use cases for performing logistic regression.
> - Explain how logistic regression models use the sigmoid function to calculate probability.
> - Compare linear regression and logistic regression.
> - Explain why logistic regression uses log loss instead of squared loss.
> - Explain the importance of regularization when training logistic regression models.

> [!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)

In the [Linear regression module](https://developers.google.com/machine-learning/crash-course/linear-regression),
you explored how to construct a model to make continuous numerical
predictions, such as the fuel efficiency of a car. But what if you want to build
a model to answer questions like "Will it rain today?" or "Is this email spam?"

This module introduces a new type of regression model called
[**logistic regression**](https://developers.google.com/machine-learning/glossary#logistic_regression)
that is designed to predict the probability of a given outcome.
[Video](https://www.youtube.com/watch?v=72AHKztZN44)

> [!IMPORTANT]
> **Key terms:**
>
> - [Logistic regression](https://developers.google.com/machine-learning/glossary#logistic_regression)

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