Logistic Regression
Page Summary
This module introduces logistic regression, a model used to predict the probability of an outcome, unlike linear regression which predicts continuous numerical values.
Logistic regression utilizes the sigmoid function to calculate probability and employs log loss as its loss function.
Regularization is crucial when training logistic regression models to prevent overfitting and improve generalization.
The module covers the comparison between linear and logistic regression and explores use cases for logistic regression.
Familiarity with introductory machine learning and linear regression concepts is assumed for this 35-minute module.
- 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.
This module assumes you are familiar with the concepts covered in the following modules:
In theLinear regression module,you explored how to construct a model to make continuous numericalpredictions, such as the fuel efficiency of a car. But what if you want to builda model to answer questions like "Will it rain today?" or "Is this email spam?"
This module introduces a new type of regression model calledlogistic regressionthat is designed to predict the probability of a given outcome.
Except as otherwise noted, the content of this page is licensed under theCreative Commons Attribution 4.0 License, and code samples are licensed under theApache 2.0 License. For details, see theGoogle Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-25 UTC.