Chapter 03.04: Logistic Regression

Logistic regression is a discriminant approach toward constructing a classifier. We will motivate logistic regression via the logistic function, define the log-loss for optimization and illustrate the approach in 1D and 2D.

Lecture video

Lecture slides

Code demo

Logistic regression & Newton-Raphson

You can run the code snippets in the demos on your local machine. The corresponding Rmd version of this demo can be found here. If you want to render the Rmd files to PDF, you need the accompanying style files.

Quiz

--- shuffle_questions: false --- ## Which statements are true? - [ ] Logistic regression can be fitted by maximum likelihood estimation with numerical optimization or solved analytically. - [ ] Logistic regression follows a generative approach. - [x] In logistic regression, the parameter vector $\mathbf{\theta}$ that maximizes the model's likelihood is identical to the one minimizing its empirical risk.