Logistic Regression

Estimated module length: 35 minutes

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.

Prerequisites:

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

• Introduction to Machine Learning
• Linear regression

In the Linear regression module, 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 that is designed to predict the probability of a given outcome.

Key terms:

• Logistic regression