Logistic Regression

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.

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